Tag Archives: java

Security updates for Friday

Post Syndicated from jake original https://lwn.net/Articles/740997/rss

Security updates have been issued by Arch Linux (chromium and vlc), Debian (erlang), Mageia (ffmpeg, tor, and wireshark), openSUSE (chromium, opensaml, openssh, openvswitch, and php7), Oracle (postgresql), Red Hat (chromium-browser, postgresql, rh-postgresql94-postgresql, rh-postgresql95-postgresql, and rh-postgresql96-postgresql), SUSE (firefox, java-1_6_0-ibm, opensaml, and xen), and Ubuntu (kernel, linux, linux-aws, linux-kvm, linux-raspi2, linux-snapdragon, linux, linux-raspi2, linux-azure, linux-gcp, linux-hwe, linux-lts-trusty, linux-lts-xenial, linux-aws, and rsync).

timeShift(GrafanaBuzz, 1w) Issue 25

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2017/12/08/timeshiftgrafanabuzz-1w-issue-25/

Welcome to TimeShift

This week, a few of us from Grafana Labs, along with 4,000 of our closest friends, headed down to chilly Austin, TX for KubeCon + CloudNativeCon North America 2017. We got to see a number of great talks and were thrilled to see Grafana make appearances in some of the presentations. We were also a sponsor of the conference and handed out a ton of swag (we overnighted some of our custom Grafana scarves, which came in handy for Thursday’s snow).

We also announced Grafana Labs has joined the Cloud Native Computing Foundation as a Silver member! We’re excited to share our expertise in time series data visualization and open source software with the CNCF community.


Latest Release

Grafana 4.6.2 is available and includes some bug fixes:

  • Prometheus: Fixes bug with new Prometheus alerts in Grafana. Make sure to download this version if you’re using Prometheus for alerting. More details in the issue. #9777
  • Color picker: Bug after using textbox input field to change/paste color string #9769
  • Cloudwatch: build using golang 1.9.2 #9667, thanks @mtanda
  • Heatmap: Fixed tooltip for “time series buckets” mode #9332
  • InfluxDB: Fixed query editor issue when using > or < operators in WHERE clause #9871

Download Grafana 4.6.2 Now


From the Blogosphere

Grafana Labs Joins the CNCF: Grafana Labs has officially joined the Cloud Native Computing Foundation (CNCF). We look forward to working with the CNCF community to democratize metrics and help unify traditionally disparate information.

Automating Web Performance Regression Alerts: Peter and his team needed a faster and easier way to find web performance regressions at the Wikimedia Foundation. Grafana 4’s alerting features were exactly what they needed. This post covers their journey on setting up alerts for both RUM and synthetic testing and shares the alerts they’ve set up on their dashboards.

How To Install Grafana on Ubuntu 17.10: As you probably guessed from the title, this article walks you through installing and configuring Grafana in the latest version of Ubuntu (or earlier releases). It also covers installing plugins using the Grafana CLI tool.

Prometheus: Starting the Server with Alertmanager, cAdvisor and Grafana: Learn how to monitor Docker from scratch using cAdvisor, Prometheus and Grafana in this detailed, step-by-step walkthrough.

Monitoring Java EE Servers with Prometheus and Payara: In this screencast, Adam uses firehose; a Java EE 7+ metrics gateway for Prometheus, to convert the JSON output into Prometheus statistics and visualizes the data in Grafana.

Monitoring Spark Streaming with InfluxDB and Grafana: This article focuses on how to monitor Apache Spark Streaming applications with InfluxDB and Grafana at scale.


GrafanaCon EU, March 1-2, 2018

We are currently reaching out to everyone who submitted a talk to GrafanaCon and will soon publish the final schedule at grafanacon.org.

Join us March 1-2, 2018 in Amsterdam for 2 days of talks centered around Grafana and the surrounding monitoring ecosystem including Graphite, Prometheus, InfluxData, Elasticsearch, Kubernetes, and more.

Get Your Ticket Now


Grafana Plugins

Lots of plugin updates and a new OpenNMS Helm App plugin to announce! To install or update any plugin in an on-prem Grafana instance, use the Grafana-cli tool, or install and update with 1 click on Hosted Grafana.

NEW PLUGIN

OpenNMS Helm App – The new OpenNMS Helm App plugin replaces the old OpenNMS data source. Helm allows users to create flexible dashboards using both fault management (FM) and performance management (PM) data from OpenNMS® Horizon™ and/or OpenNMS® Meridian™. The old data source is now deprecated.


Install Now

UPDATED PLUGIN

PNP Data Source – This data source plugin (that uses PNP4Nagios to access RRD files) received a small, but important update that fixes template query parsing.


Update

UPDATED PLUGIN

Vonage Status Panel – The latest version of the Status Panel comes with a number of small fixes and changes. Below are a few of the enhancements:

  • Threshold settings – removed Show Always option, and replaced it with 2 options:
    • Display Alias – Select when to show the metric alias.
    • Display Value – Select when to show the metric value.
  • Text format configuration (bold / italic) for warning / critical / disabled states.
  • Option to change the corner radius of the panel. Now you can change the panel’s shape to have rounded corners.

Update

UPDATED PLUGIN

Google Calendar Plugin – This plugin received a small update, so be sure to install version 1.0.4.


Update

UPDATED PLUGIN

Carpet Plot Panel – The Carpet Plot Panel received a fix for IE 11, and also added the ability to choose custom colors.


Update


Upcoming Events:

In between code pushes we like to speak at, sponsor and attend all kinds of conferences and meetups. We also like to make sure we mention other Grafana-related events happening all over the world. If you’re putting on just such an event, let us know and we’ll list it here.

Docker Meetup @ Tuenti | Madrid, Spain – Dec 12, 2017: Javier Provecho: Intro to Metrics with Swarm, Prometheus and Grafana

Learn how to gain visibility in real time for your micro services. We’ll cover how to deploy a Prometheus server with persistence and Grafana, how to enable metrics endpoints for various service types (docker daemon, traefik proxy and postgres) and how to scrape, visualize and set up alarms based on those metrics.

RSVP

Grafana Lyon Meetup n ° 2 | Lyon, France – Dec 14, 2017: This meetup will cover some of the latest innovations in Grafana and discussion about automation. Also, free beer and chips, so – of course you’re going!

RSVP

FOSDEM | Brussels, Belgium – Feb 3-4, 2018: FOSDEM is a free developer conference where thousands of developers of free and open source software gather to share ideas and technology. Carl Bergquist is managing the Cloud and Monitoring Devroom, and we’ve heard there were some great talks submitted. There is no need to register; all are welcome.


Tweet of the Week

We scour Twitter each week to find an interesting/beautiful dashboard and show it off! #monitoringLove

We were thrilled to see our dashboards bigger than life at KubeCon + CloudNativeCon this week. Thanks for snapping a photo and sharing!


Grafana Labs is Hiring!

We are passionate about open source software and thrive on tackling complex challenges to build the future. We ship code from every corner of the globe and love working with the community. If this sounds exciting, you’re in luck – WE’RE HIRING!

Check out our Open Positions


How are we doing?

Hard to believe this is the 25th issue of Timeshift! I have a blast writing these roundups, but Let me know what you think. Submit a comment on this article below, or post something at our community forum. Find an article I haven’t included? Send it my way. Help us make timeShift better!

Follow us on Twitter, like us on Facebook, and join the Grafana Labs community.

Security updates for Thursday

Post Syndicated from jake original https://lwn.net/Articles/740883/rss

Security updates have been issued by CentOS (firefox, java-1.7.0-openjdk, kernel, liblouis, qemu-kvm, sssd, and thunderbird), Debian (heimdal and nova), openSUSE (shibboleth-sp), Oracle (java-1.7.0-openjdk), Red Hat (Red Hat OpenShift Enterprise), Scientific Linux (openafs), SUSE (kernel), and Ubuntu (rsync).

How to Easily Apply Amazon Cloud Directory Schema Changes with In-Place Schema Upgrades

Post Syndicated from Mahendra Chheda original https://aws.amazon.com/blogs/security/how-to-easily-apply-amazon-cloud-directory-schema-changes-with-in-place-schema-upgrades/

Now, Amazon Cloud Directory makes it easier for you to apply schema changes across your directories with in-place schema upgrades. Your directory now remains available while Cloud Directory applies backward-compatible schema changes such as the addition of new fields. Without migrating data between directories or applying code changes to your applications, you can upgrade your schemas. You also can view the history of your schema changes in Cloud Directory by using version identifiers, which help you track and audit schema versions across directories. If you have multiple instances of a directory with the same schema, you can view the version history of schema changes to manage your directory fleet and ensure that all directories are running with the same schema version.

In this blog post, I demonstrate how to perform an in-place schema upgrade and use schema versions in Cloud Directory. I add additional attributes to an existing facet and add a new facet to a schema. I then publish the new schema and apply it to running directories, upgrading the schema in place. I also show how to view the version history of a directory schema, which helps me to ensure my directory fleet is running the same version of the schema and has the correct history of schema changes applied to it.

Note: I share Java code examples in this post. I assume that you are familiar with the AWS SDK and can use Java-based code to build a Cloud Directory code example. You can apply the concepts I cover in this post to other programming languages such as Python and Ruby.

Cloud Directory fundamentals

I will start by covering a few Cloud Directory fundamentals. If you are already familiar with the concepts behind Cloud Directory facets, schemas, and schema lifecycles, you can skip to the next section.

Facets: Groups of attributes. You use facets to define object types. For example, you can define a device schema by adding facets such as computers, phones, and tablets. A computer facet can track attributes such as serial number, make, and model. You can then use the facets to create computer objects, phone objects, and tablet objects in the directory to which the schema applies.

Schemas: Collections of facets. Schemas define which types of objects can be created in a directory (such as users, devices, and organizations) and enforce validation of data for each object class. All data within a directory must conform to the applied schema. As a result, the schema definition is essentially a blueprint to construct a directory with an applied schema.

Schema lifecycle: The four distinct states of a schema: Development, Published, Applied, and Deleted. Schemas in the Published and Applied states have version identifiers and cannot be changed. Schemas in the Applied state are used by directories for validation as applications insert or update data. You can change schemas in the Development state as many times as you need them to. In-place schema upgrades allow you to apply schema changes to an existing Applied schema in a production directory without the need to export and import the data populated in the directory.

How to add attributes to a computer inventory application schema and perform an in-place schema upgrade

To demonstrate how to set up schema versioning and perform an in-place schema upgrade, I will use an example of a computer inventory application that uses Cloud Directory to store relationship data. Let’s say that at my company, AnyCompany, we use this computer inventory application to track all computers we give to our employees for work use. I previously created a ComputerSchema and assigned its version identifier as 1. This schema contains one facet called ComputerInfo that includes attributes for SerialNumber, Make, and Model, as shown in the following schema details.

Schema: ComputerSchema
Version: 1

Facet: ComputerInfo
Attribute: SerialNumber, type: Integer
Attribute: Make, type: String
Attribute: Model, type: String

AnyCompany has offices in Seattle, Portland, and San Francisco. I have deployed the computer inventory application for each of these three locations. As shown in the lower left part of the following diagram, ComputerSchema is in the Published state with a version of 1. The Published schema is applied to SeattleDirectory, PortlandDirectory, and SanFranciscoDirectory for AnyCompany’s three locations. Implementing separate directories for different geographic locations when you don’t have any queries that cross location boundaries is a good data partitioning strategy and gives your application better response times with lower latency.

Diagram of ComputerSchema in Published state and applied to three directories

Legend for the diagrams in this post

The following code example creates the schema in the Development state by using a JSON file, publishes the schema, and then creates directories for the Seattle, Portland, and San Francisco locations. For this example, I assume the schema has been defined in the JSON file. The createSchema API creates a schema Amazon Resource Name (ARN) with the name defined in the variable, SCHEMA_NAME. I can use the putSchemaFromJson API to add specific schema definitions from the JSON file.

// The utility method to get valid Cloud Directory schema JSON
String validJson = getJsonFile("ComputerSchema_version_1.json")

String SCHEMA_NAME = "ComputerSchema";

String developmentSchemaArn = client.createSchema(new CreateSchemaRequest()
        .withName(SCHEMA_NAME))
        .getSchemaArn();

// Put the schema document in the Development schema
PutSchemaFromJsonResult result = client.putSchemaFromJson(new PutSchemaFromJsonRequest()
        .withSchemaArn(developmentSchemaArn)
        .withDocument(validJson));

The following code example takes the schema that is currently in the Development state and publishes the schema, changing its state to Published.

String SCHEMA_VERSION = "1";
String publishedSchemaArn = client.publishSchema(
        new PublishSchemaRequest()
        .withDevelopmentSchemaArn(developmentSchemaArn)
        .withVersion(SCHEMA_VERSION))
        .getPublishedSchemaArn();

// Our Published schema ARN is as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1

The following code example creates a directory named SeattleDirectory and applies the published schema. The createDirectory API call creates a directory by using the published schema provided in the API parameters. Note that Cloud Directory stores a version of the schema in the directory in the Applied state. I will use similar code to create directories for PortlandDirectory and SanFranciscoDirectory.

String DIRECTORY_NAME = "SeattleDirectory"; 

CreateDirectoryResult directory = client.createDirectory(
        new CreateDirectoryRequest()
        .withName(DIRECTORY_NAME)
        .withSchemaArn(publishedSchemaArn));

String directoryArn = directory.getDirectoryArn();
String appliedSchemaArn = directory.getAppliedSchemaArn();

// This code section can be reused to create directories for Portland and San Francisco locations with the appropriate directory names

// Our directory ARN is as follows 
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX

// Our applied schema ARN is as follows 
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1

Revising a schema

Now let’s say my company, AnyCompany, wants to add more information for computers and to track which employees have been assigned a computer for work use. I modify the schema to add two attributes to the ComputerInfo facet: Description and OSVersion (operating system version). I make Description optional because it is not important for me to track this attribute for the computer objects I create. I make OSVersion mandatory because it is critical for me to track it for all computer objects so that I can make changes such as applying security patches or making upgrades. Because I make OSVersion mandatory, I must provide a default value that Cloud Directory will apply to objects that were created before the schema revision, in order to handle backward compatibility. Note that you can replace the value in any object with a different value.

I also add a new facet to track computer assignment information, shown in the following updated schema as the ComputerAssignment facet. This facet tracks these additional attributes: Name (the name of the person to whom the computer is assigned), EMail (the email address of the assignee), Department, and department CostCenter. Note that Cloud Directory refers to the previously available version identifier as the Major Version. Because I can now add a minor version to a schema, I also denote the changed schema as Minor Version A.

Schema: ComputerSchema
Major Version: 1
Minor Version: A 

Facet: ComputerInfo
Attribute: SerialNumber, type: Integer 
Attribute: Make, type: String
Attribute: Model, type: Integer
Attribute: Description, type: String, required: NOT_REQUIRED
Attribute: OSVersion, type: String, required: REQUIRED_ALWAYS, default: "Windows 7"

Facet: ComputerAssignment
Attribute: Name, type: String
Attribute: EMail, type: String
Attribute: Department, type: String
Attribute: CostCenter, type: Integer

The following diagram shows the changes that were made when I added another facet to the schema and attributes to the existing facet. The highlighted area of the diagram (bottom left) shows that the schema changes were published.

Diagram showing that schema changes were published

The following code example revises the existing Development schema by adding the new attributes to the ComputerInfo facet and by adding the ComputerAssignment facet. I use a new JSON file for the schema revision, and for the purposes of this example, I am assuming the JSON file has the full schema including planned revisions.

// The utility method to get a valid CloudDirectory schema JSON
String schemaJson = getJsonFile("ComputerSchema_version_1_A.json")

// Put the schema document in the Development schema
PutSchemaFromJsonResult result = client.putSchemaFromJson(
        new PutSchemaFromJsonRequest()
        .withSchemaArn(developmentSchemaArn)
        .withDocument(schemaJson));

Upgrading the Published schema

The following code example performs an in-place schema upgrade of the Published schema with schema revisions (it adds new attributes to the existing facet and another facet to the schema). The upgradePublishedSchema API upgrades the Published schema with backward-compatible changes from the Development schema.

// From an earlier code example, I know the publishedSchemaArn has this value: "arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1"

// Upgrade publishedSchemaArn to minorVersion A. The Development schema must be backward compatible with 
// the existing publishedSchemaArn. 

String minorVersion = "A"

UpgradePublishedSchemaResult upgradePublishedSchemaResult = client.upgradePublishedSchema(new UpgradePublishedSchemaRequest()
        .withDevelopmentSchemaArn(developmentSchemaArn)
        .withPublishedSchemaArn(publishedSchemaArn)
        .withMinorVersion(minorVersion));

String upgradedPublishedSchemaArn = upgradePublishedSchemaResult.getUpgradedSchemaArn();

// The Published schema ARN after the upgrade shows a minor version as follows 
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:schema/published/ComputerSchema/1/A

Upgrading the Applied schema

The following diagram shows the in-place schema upgrade for the SeattleDirectory directory. I am performing the schema upgrade so that I can reflect the new schemas in all three directories. As a reminder, I added new attributes to the ComputerInfo facet and also added the ComputerAssignment facet. After the schema and directory upgrade, I can create objects for the ComputerInfo and ComputerAssignment facets in the SeattleDirectory. Any objects that were created with the old facet definition for ComputerInfo will now use the default values for any additional attributes defined in the new schema.

Diagram of the in-place schema upgrade for the SeattleDirectory directory

I use the following code example to perform an in-place upgrade of the SeattleDirectory to a Major Version of 1 and a Minor Version of A. Note that you should change a Major Version identifier in a schema to make backward-incompatible changes such as changing the data type of an existing attribute or dropping a mandatory attribute from your schema. Backward-incompatible changes require directory data migration from a previous version to the new version. You should change a Minor Version identifier in a schema to make backward-compatible upgrades such as adding additional attributes or adding facets, which in turn may contain one or more attributes. The upgradeAppliedSchema API lets me upgrade an existing directory with a different version of a schema.

// This upgrades ComputerSchema version 1 of the Applied schema in SeattleDirectory to Major Version 1 and Minor Version A
// The schema must be backward compatible or the API will fail with IncompatibleSchemaException

UpgradeAppliedSchemaResult upgradeAppliedSchemaResult = client.upgradeAppliedSchema(new UpgradeAppliedSchemaRequest()
        .withDirectoryArn(directoryArn)
        .withPublishedSchemaArn(upgradedPublishedSchemaArn));

String upgradedAppliedSchemaArn = upgradeAppliedSchemaResult.getUpgradedSchemaArn();

// The Applied schema ARN after the in-place schema upgrade will appear as follows
// arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1

// This code section can be reused to upgrade directories for the Portland and San Francisco locations with the appropriate directory ARN

Note: Cloud Directory has excluded returning the Minor Version identifier in the Applied schema ARN for backward compatibility and to enable the application to work across older and newer versions of the directory.

The following diagram shows the changes that are made when I perform an in-place schema upgrade in the two remaining directories, PortlandDirectory and SanFranciscoDirectory. I make these calls sequentially, upgrading PortlandDirectory first and then upgrading SanFranciscoDirectory. I use the same code example that I used earlier to upgrade SeattleDirectory. Now, all my directories are running the most current version of the schema. Also, I made these schema changes without having to migrate data and while maintaining my application’s high availability.

Diagram showing the changes that are made with an in-place schema upgrade in the two remaining directories

Schema revision history

I can now view the schema revision history for any of AnyCompany’s directories by using the listAppliedSchemaArns API. Cloud Directory maintains the five most recent versions of applied schema changes. Similarly, to inspect the current Minor Version that was applied to my schema, I use the getAppliedSchemaVersion API. The listAppliedSchemaArns API returns the schema ARNs based on my schema filter as defined in withSchemaArn.

I use the following code example to query an Applied schema for its version history.

// This returns the five most recent Minor Versions associated with a Major Version
ListAppliedSchemaArnsResult listAppliedSchemaArnsResult = client.listAppliedSchemaArns(new ListAppliedSchemaArnsRequest()
        .withDirectoryArn(directoryArn)
        .withSchemaArn(upgradedAppliedSchemaArn));

// Note: The listAppliedSchemaArns API without the SchemaArn filter returns all the Major Versions in a directory

The listAppliedSchemaArns API returns the two ARNs as shown in the following output.

arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1
arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1/A

The following code example queries an Applied schema for current Minor Version by using the getAppliedSchemaVersion API.

// This returns the current Applied schema's Minor Version ARN 

GetAppliedSchemaVersion getAppliedSchemaVersionResult = client.getAppliedSchemaVersion(new GetAppliedSchemaVersionRequest()
	.withSchemaArn(upgradedAppliedSchemaArn));

The getAppliedSchemaVersion API returns the current Applied schema ARN with a Minor Version, as shown in the following output.

arn:aws:clouddirectory:us-west-2:XXXXXXXXXXXX:directory/XX_DIRECTORY_GUID_XX/schema/ComputerSchema/1/A

If you have a lot of directories, schema revision API calls can help you audit your directory fleet and ensure that all directories are running the same version of a schema. Such auditing can help you ensure high integrity of directories across your fleet.

Summary

You can use in-place schema upgrades to make changes to your directory schema as you evolve your data set to match the needs of your application. An in-place schema upgrade allows you to maintain high availability for your directory and applications while the upgrade takes place. For more information about in-place schema upgrades, see the in-place schema upgrade documentation.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about implementing the solution in this post, start a new thread in the Directory Service forum or contact AWS Support.

– Mahendra

 

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/740799/rss

Security updates have been issued by CentOS (samba4), Mageia (libxcursor and libxfont/libxfont2), openSUSE (exim, GraphicsMagick, graphviz, pdns, and pdns-recursor), Oracle (firefox and liblouis), Red Hat (java-1.7.0-openjdk), Scientific Linux (java-1.7.0-openjdk), SUSE (firefox, shibboleth-sp, and xen), and Ubuntu (linux-firmware).

Implementing Dynamic ETL Pipelines Using AWS Step Functions

Post Syndicated from Tara Van Unen original https://aws.amazon.com/blogs/compute/implementing-dynamic-etl-pipelines-using-aws-step-functions/

This post contributed by:
Wangechi Dole, AWS Solutions Architect
Milan Krasnansky, ING, Digital Solutions Developer, SGK
Rian Mookencherry, Director – Product Innovation, SGK

Data processing and transformation is a common use case you see in our customer case studies and success stories. Often, customers deal with complex data from a variety of sources that needs to be transformed and customized through a series of steps to make it useful to different systems and stakeholders. This can be difficult due to the ever-increasing volume, velocity, and variety of data. Today, data management challenges cannot be solved with traditional databases.

Workflow automation helps you build solutions that are repeatable, scalable, and reliable. You can use AWS Step Functions for this. A great example is how SGK used Step Functions to automate the ETL processes for their client. With Step Functions, SGK has been able to automate changes within the data management system, substantially reducing the time required for data processing.

In this post, SGK shares the details of how they used Step Functions to build a robust data processing system based on highly configurable business transformation rules for ETL processes.

SGK: Building dynamic ETL pipelines

SGK is a subsidiary of Matthews International Corporation, a diversified organization focusing on brand solutions and industrial technologies. SGK’s Global Content Creation Studio network creates compelling content and solutions that connect brands and products to consumers through multiple assets including photography, video, and copywriting.

We were recently contracted to build a sophisticated and scalable data management system for one of our clients. We chose to build the solution on AWS to leverage advanced, managed services that help to improve the speed and agility of development.

The data management system served two main functions:

  1. Ingesting a large amount of complex data to facilitate both reporting and product funding decisions for the client’s global marketing and supply chain organizations.
  2. Processing the data through normalization and applying complex algorithms and data transformations. The system goal was to provide information in the relevant context—such as strategic marketing, supply chain, product planning, etc. —to the end consumer through automated data feeds or updates to existing ETL systems.

We were faced with several challenges:

  • Output data that needed to be refreshed at least twice a day to provide fresh datasets to both local and global markets. That constant data refresh posed several challenges, especially around data management and replication across multiple databases.
  • The complexity of reporting business rules that needed to be updated on a constant basis.
  • Data that could not be processed as contiguous blocks of typical time-series data. The measurement of the data was done across seasons (that is, combination of dates), which often resulted with up to three overlapping seasons at any given time.
  • Input data that came from 10+ different data sources. Each data source ranged from 1–20K rows with as many as 85 columns per input source.

These challenges meant that our small Dev team heavily invested time in frequent configuration changes to the system and data integrity verification to make sure that everything was operating properly. Maintaining this system proved to be a daunting task and that’s when we turned to Step Functions—along with other AWS services—to automate our ETL processes.

Solution overview

Our solution included the following AWS services:

  • AWS Step Functions: Before Step Functions was available, we were using multiple Lambda functions for this use case and running into memory limit issues. With Step Functions, we can execute steps in parallel simultaneously, in a cost-efficient manner, without running into memory limitations.
  • AWS Lambda: The Step Functions state machine uses Lambda functions to implement the Task states. Our Lambda functions are implemented in Java 8.
  • Amazon DynamoDB provides us with an easy and flexible way to manage business rules. We specify our rules as Keys. These are key-value pairs stored in a DynamoDB table.
  • Amazon RDS: Our ETL pipelines consume source data from our RDS MySQL database.
  • Amazon Redshift: We use Amazon Redshift for reporting purposes because it integrates with our BI tools. Currently we are using Tableau for reporting which integrates well with Amazon Redshift.
  • Amazon S3: We store our raw input files and intermediate results in S3 buckets.
  • Amazon CloudWatch Events: Our users expect results at a specific time. We use CloudWatch Events to trigger Step Functions on an automated schedule.

Solution architecture

This solution uses a declarative approach to defining business transformation rules that are applied by the underlying Step Functions state machine as data moves from RDS to Amazon Redshift. An S3 bucket is used to store intermediate results. A CloudWatch Event rule triggers the Step Functions state machine on a schedule. The following diagram illustrates our architecture:

Here are more details for the above diagram:

  1. A rule in CloudWatch Events triggers the state machine execution on an automated schedule.
  2. The state machine invokes the first Lambda function.
  3. The Lambda function deletes all existing records in Amazon Redshift. Depending on the dataset, the Lambda function can create a new table in Amazon Redshift to hold the data.
  4. The same Lambda function then retrieves Keys from a DynamoDB table. Keys represent specific marketing campaigns or seasons and map to specific records in RDS.
  5. The state machine executes the second Lambda function using the Keys from DynamoDB.
  6. The second Lambda function retrieves the referenced dataset from RDS. The records retrieved represent the entire dataset needed for a specific marketing campaign.
  7. The second Lambda function executes in parallel for each Key retrieved from DynamoDB and stores the output in CSV format temporarily in S3.
  8. Finally, the Lambda function uploads the data into Amazon Redshift.

To understand the above data processing workflow, take a closer look at the Step Functions state machine for this example.

We walk you through the state machine in more detail in the following sections.

Walkthrough

To get started, you need to:

  • Create a schedule in CloudWatch Events
  • Specify conditions for RDS data extracts
  • Create Amazon Redshift input files
  • Load data into Amazon Redshift

Step 1: Create a schedule in CloudWatch Events
Create rules in CloudWatch Events to trigger the Step Functions state machine on an automated schedule. The following is an example cron expression to automate your schedule:

In this example, the cron expression invokes the Step Functions state machine at 3:00am and 2:00pm (UTC) every day.

Step 2: Specify conditions for RDS data extracts
We use DynamoDB to store Keys that determine which rows of data to extract from our RDS MySQL database. An example Key is MCS2017, which stands for, Marketing Campaign Spring 2017. Each campaign has a specific start and end date and the corresponding dataset is stored in RDS MySQL. A record in RDS contains about 600 columns, and each Key can represent up to 20K records.

A given day can have multiple campaigns with different start and end dates running simultaneously. In the following example DynamoDB item, three campaigns are specified for the given date.

The state machine example shown above uses Keys 31, 32, and 33 in the first ChoiceState and Keys 21 and 22 in the second ChoiceState. These keys represent marketing campaigns for a given day. For example, on Monday, there are only two campaigns requested. The ChoiceState with Keys 21 and 22 is executed. If three campaigns are requested on Tuesday, for example, then ChoiceState with Keys 31, 32, and 33 is executed. MCS2017 can be represented by Key 21 and Key 33 on Monday and Tuesday, respectively. This approach gives us the flexibility to add or remove campaigns dynamically.

Step 3: Create Amazon Redshift input files
When the state machine begins execution, the first Lambda function is invoked as the resource for FirstState, represented in the Step Functions state machine as follows:

"Comment": ” AWS Amazon States Language.", 
  "StartAt": "FirstState",
 
"States": { 
  "FirstState": {
   
"Type": "Task",
   
"Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Start",
    "Next": "ChoiceState" 
  } 

As described in the solution architecture, the purpose of this Lambda function is to delete existing data in Amazon Redshift and retrieve keys from DynamoDB. In our use case, we found that deleting existing records was more efficient and less time-consuming than finding the delta and updating existing records. On average, an Amazon Redshift table can contain about 36 million cells, which translates to roughly 65K records. The following is the code snippet for the first Lambda function in Java 8:

public class LambdaFunctionHandler implements RequestHandler<Map<String,Object>,Map<String,String>> {
    Map<String,String> keys= new HashMap<>();
    public Map<String, String> handleRequest(Map<String, Object> input, Context context){
       Properties config = getConfig(); 
       // 1. Cleaning Redshift Database
       new RedshiftDataService(config).cleaningTable(); 
       // 2. Reading data from Dynamodb
       List<String> keyList = new DynamoDBDataService(config).getCurrentKeys();
       for(int i = 0; i < keyList.size(); i++) {
           keys.put(”key" + (i+1), keyList.get(i)); 
       }
       keys.put(”key" + T,String.valueOf(keyList.size()));
       // 3. Returning the key values and the key count from the “for” loop
       return (keys);
}

The following JSON represents ChoiceState.

"ChoiceState": {
   "Type" : "Choice",
   "Choices": [ 
   {

      "Variable": "$.keyT",
     "StringEquals": "3",
     "Next": "CurrentThreeKeys" 
   }, 
   {

     "Variable": "$.keyT",
    "StringEquals": "2",
    "Next": "CurrentTwooKeys" 
   } 
 ], 
 "Default": "DefaultState"
}

The variable $.keyT represents the number of keys retrieved from DynamoDB. This variable determines which of the parallel branches should be executed. At the time of publication, Step Functions does not support dynamic parallel state. Therefore, choices under ChoiceState are manually created and assigned hardcoded StringEquals values. These values represent the number of parallel executions for the second Lambda function.

For example, if $.keyT equals 3, the second Lambda function is executed three times in parallel with keys, $key1, $key2 and $key3 retrieved from DynamoDB. Similarly, if $.keyT equals two, the second Lambda function is executed twice in parallel.  The following JSON represents this parallel execution:

"CurrentThreeKeys": { 
  "Type": "Parallel",
  "Next": "NextState",
  "Branches": [ 
  {

     "StartAt": “key31",
    "States": { 
       “key31": {

          "Type": "Task",
        "InputPath": "$.key1",
        "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
        "End": true 
       } 
    } 
  }, 
  {

     "StartAt": “key32",
    "States": { 
     “key32": {

        "Type": "Task",
       "InputPath": "$.key2",
         "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
       "End": true 
      } 
     } 
   }, 
   {

      "StartAt": “key33",
       "States": { 
          “key33": {

                "Type": "Task",
             "InputPath": "$.key3",
             "Resource": "arn:aws:lambda:xx-xxxx-x:XXXXXXXXXXXX:function:Execution",
           "End": true 
       } 
     } 
    } 
  ] 
} 

Step 4: Load data into Amazon Redshift
The second Lambda function in the state machine extracts records from RDS associated with keys retrieved for DynamoDB. It processes the data then loads into an Amazon Redshift table. The following is code snippet for the second Lambda function in Java 8.

public class LambdaFunctionHandler implements RequestHandler<String, String> {
 public static String key = null;

public String handleRequest(String input, Context context) { 
   key=input; 
   //1. Getting basic configurations for the next classes + s3 client Properties
   config = getConfig();

   AmazonS3 s3 = AmazonS3ClientBuilder.defaultClient(); 
   // 2. Export query results from RDS into S3 bucket 
   new RdsDataService(config).exportDataToS3(s3,key); 
   // 3. Import query results from S3 bucket into Redshift 
    new RedshiftDataService(config).importDataFromS3(s3,key); 
   System.out.println(input); 
   return "SUCCESS"; 
 } 
}

After the data is loaded into Amazon Redshift, end users can visualize it using their preferred business intelligence tools.

Lessons learned

  • At the time of publication, the 1.5–GB memory hard limit for Lambda functions was inadequate for processing our complex workload. Step Functions gave us the flexibility to chunk our large datasets and process them in parallel, saving on costs and time.
  • In our previous implementation, we assigned each key a dedicated Lambda function along with CloudWatch rules for schedule automation. This approach proved to be inefficient and quickly became an operational burden. Previously, we processed each key sequentially, with each key adding about five minutes to the overall processing time. For example, processing three keys meant that the total processing time was three times longer. With Step Functions, the entire state machine executes in about five minutes.
  • Using DynamoDB with Step Functions gave us the flexibility to manage keys efficiently. In our previous implementations, keys were hardcoded in Lambda functions, which became difficult to manage due to frequent updates. DynamoDB is a great way to store dynamic data that changes frequently, and it works perfectly with our serverless architectures.

Conclusion

With Step Functions, we were able to fully automate the frequent configuration updates to our dataset resulting in significant cost savings, reduced risk to data errors due to system downtime, and more time for us to focus on new product development rather than support related issues. We hope that you have found the information useful and that it can serve as a jump-start to building your own ETL processes on AWS with managed AWS services.

For more information about how Step Functions makes it easy to coordinate the components of distributed applications and microservices in any workflow, see the use case examples and then build your first state machine in under five minutes in the Step Functions console.

If you have questions or suggestions, please comment below.

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/740721/rss

Security updates have been issued by Debian (libextractor), Fedora (java-9-openjdk, kernel, python, and qt5-qtwebengine), Oracle (sssd and thunderbird), Red Hat (firefox, liblouis, and sssd), Scientific Linux (firefox, liblouis, and sssd), and Ubuntu (libxml2).

AWS Cloud9 – Cloud Developer Environments

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-cloud9-cloud-developer-environments/

One of the first things you learn when you start programming is that, just like any craftsperson, your tools matter. Notepad.exe isn’t going to cut it. A powerful editor and testing pipeline supercharge your productivity. I still remember learning to use Vim for the first time and being able to zip around systems and complex programs. Do you remember how hard it was to setup all your compilers and dependencies on a new machine? How many cycles have you wasted matching versions, tinkering with configs, and then writing documentation to onboard a new developer to a project?

Today we’re launching AWS Cloud9, an Integrated Development Environment (IDE) for writing, running, and debugging code, all from your web browser. Cloud9 comes prepackaged with essential tools for many popular programming languages (Javascript, Python, PHP, etc.) so you don’t have to tinker with installing various compilers and toolchains. Cloud9 also provides a seamless experience for working with serverless applications allowing you to quickly switch between local and remote testing or debugging. Based on the popular open source Ace Editor and c9.io IDE (which we acquired last year), AWS Cloud9 is designed to make collaborative cloud development easy with extremely powerful pair programming features. There are more features than I could ever cover in this post but to give a quick breakdown I’ll break the IDE into 3 components: The editor, the AWS integrations, and the collaboration.

Editing


The Ace Editor at the core of Cloud9 is what lets you write code quickly, easily, and beautifully. It follows a UNIX philosophy of doing one thing and doing it well: writing code.

It has all the typical IDE features you would expect: live syntax checking, auto-indent, auto-completion, code folding, split panes, version control integration, multiple cursors and selections, and it also has a few unique features I want to highlight. First of all, it’s fast, even for large (100000+ line) files. There’s no lag or other issues while typing. It has over two dozen themes built-in (solarized!) and you can bring all of your favorite themes from Sublime Text or TextMate as well. It has built-in support for 40+ language modes and customizable run configurations for your projects. Most importantly though, it has Vim mode (or emacs if your fingers work that way). It also has a keybinding editor that allows you to bend the editor to your will.

The editor supports powerful keyboard navigation and commands (similar to Sublime Text or vim plugins like ctrlp). On a Mac, with ⌘+P you can open any file in your environment with fuzzy search. With ⌘+. you can open up the command pane which allows you to do invoke any of the editor commands by typing the name. It also helpfully displays the keybindings for a command in the pane, for instance to open to a terminal you can press ⌥+T. Oh, did I mention there’s a terminal? It ships with the AWS CLI preconfigured for access to your resources.

The environment also comes with pre-installed debugging tools for many popular languages – but you’re not limited to what’s already installed. It’s easy to add in new programs and define new run configurations.

The editor is just one, admittedly important, component in an IDE though. I want to show you some other compelling features.

AWS Integrations

The AWS Cloud9 IDE is the first IDE I’ve used that is truly “cloud native”. The service is provided at no additional charge, and you only charged for the underlying compute and storage resources. When you create an environment you’re prompted for either: an instance type and an auto-hibernate time, or SSH access to a machine of your choice.

If you’re running in AWS the auto-hibernate feature will stop your instance shortly after you stop using your IDE. This can be a huge cost savings over running a more permanent developer desktop. You can also launch it within a VPC to give it secure access to your development resources. If you want to run Cloud9 outside of AWS, or on an existing instance, you can provide SSH access to the service which it will use to create an environment on the external machine. Your environment is provisioned with automatic and secure access to your AWS account so you don’t have to worry about copying credentials around. Let me say that again: you can run this anywhere.

Serverless Development with AWS Cloud9

I spend a lot of time on Twitch developing serverless applications. I have hundreds of lambda functions and APIs deployed. Cloud9 makes working with every single one of these functions delightful. Let me show you how it works.


If you look in the top right side of the editor you’ll see an AWS Resources tab. Opening this you can see all of the lambda functions in your region (you can see functions in other regions by adjusting your region preferences in the AWS preference pane).

You can import these remote functions to your local workspace just by double-clicking them. This allows you to edit, test, and debug your serverless applications all locally. You can create new applications and functions easily as well. If you click the Lambda icon in the top right of the pane you’ll be prompted to create a new lambda function and Cloud9 will automatically create a Serverless Application Model template for you as well. The IDE ships with support for the popular SAM local tool pre-installed. This is what I use in most of my local testing and serverless development. Since you have a terminal, it’s easy to install additional tools and use other serverless frameworks.

 

Launching an Environment from AWS CodeStar

With AWS CodeStar you can easily provision an end-to-end continuous delivery toolchain for development on AWS. Codestar provides a unified experience for building, testing, deploying, and managing applications using AWS CodeCommit, CodeBuild, CodePipeline, and CodeDeploy suite of services. Now, with a few simple clicks you can provision a Cloud9 environment to develop your application. Your environment will be pre-configured with the code for your CodeStar application already checked out and git credentials already configured.

You can easily share this environment with your coworkers which leads me to another extremely useful set of features.

Collaboration

One of the many things that sets AWS Cloud9 apart from other editors are the rich collaboration tools. You can invite an IAM user to your environment with a few clicks.

You can see what files they’re working on, where their cursors are, and even share a terminal. The chat features is useful as well.

Things to Know

  • There are no additional charges for this service beyond the underlying compute and storage.
  • c9.io continues to run for existing users. You can continue to use all the features of c9.io and add new team members if you have a team account. In the future, we will provide tools for easy migration of your c9.io workspaces to AWS Cloud9.
  • AWS Cloud9 is available in the US West (Oregon), US East (Ohio), US East (N.Virginia), EU (Ireland), and Asia Pacific (Singapore) regions.

I can’t wait to see what you build with AWS Cloud9!

Randall

Glenn’s Take on re:Invent 2017 Part 1

Post Syndicated from Glenn Gore original https://aws.amazon.com/blogs/architecture/glenns-take-on-reinvent-2017-part-1/

GREETINGS FROM LAS VEGAS

Glenn Gore here, Chief Architect for AWS. I’m in Las Vegas this week — with 43K others — for re:Invent 2017. We have a lot of exciting announcements this week. I’m going to post to the AWS Architecture blog each day with my take on what’s interesting about some of the announcements from a cloud architectural perspective.

Why not start at the beginning? At the Midnight Madness launch on Sunday night, we announced Amazon Sumerian, our platform for VR, AR, and mixed reality. The hype around VR/AR has existed for many years, though for me, it is a perfect example of how a working end-to-end solution often requires innovation from multiple sources. For AR/VR to be successful, we need many components to come together in a coherent manner to provide a great experience.

First, we need lightweight, high-definition goggles with motion tracking that are comfortable to wear. Second, we need to track movement of our body and hands in a 3-D space so that we can interact with virtual objects in the virtual world. Third, we need to build the virtual world itself and populate it with assets and define how the interactions will work and connect with various other systems.

There has been rapid development of the physical devices for AR/VR, ranging from iOS devices to Oculus Rift and HTC Vive, which provide excellent capabilities for the first and second components defined above. With the launch of Amazon Sumerian we are solving for the third area, which will help developers easily build their own virtual worlds and start experimenting and innovating with how to apply AR/VR in new ways.

Already, within 48 hours of Amazon Sumerian being announced, I have had multiple discussions with customers and partners around some cool use cases where VR can help in training simulations, remote-operator controls, or with new ideas around interacting with complex visual data sets, which starts bringing concepts straight out of sci-fi movies into the real (virtual) world. I am really excited to see how Sumerian will unlock the creative potential of developers and where this will lead.

Amazon MQ
I am a huge fan of distributed architectures where asynchronous messaging is the backbone of connecting the discrete components together. Amazon Simple Queue Service (Amazon SQS) is one of my favorite services due to its simplicity, scalability, performance, and the incredible flexibility of how you can use Amazon SQS in so many different ways to solve complex queuing scenarios.

While Amazon SQS is easy to use when building cloud-native applications on AWS, many of our customers running existing applications on-premises required support for different messaging protocols such as: Java Message Service (JMS), .Net Messaging Service (NMS), Advanced Message Queuing Protocol (AMQP), MQ Telemetry Transport (MQTT), Simple (or Streaming) Text Orientated Messaging Protocol (STOMP), and WebSockets. One of the most popular applications for on-premise message brokers is Apache ActiveMQ. With the release of Amazon MQ, you can now run Apache ActiveMQ on AWS as a managed service similar to what we did with Amazon ElastiCache back in 2012. For me, there are two compelling, major benefits that Amazon MQ provides:

  • Integrate existing applications with cloud-native applications without having to change a line of application code if using one of the supported messaging protocols. This removes one of the biggest blockers for integration between the old and the new.
  • Remove the complexity of configuring Multi-AZ resilient message broker services as Amazon MQ provides out-of-the-box redundancy by always storing messages redundantly across Availability Zones. Protection is provided against failure of a broker through to complete failure of an Availability Zone.

I believe that Amazon MQ is a major component in the tools required to help you migrate your existing applications to AWS. Having set up cross-data center Apache ActiveMQ clusters in the past myself and then testing to ensure they work as expected during critical failure scenarios, technical staff working on migrations to AWS benefit from the ease of deploying a fully redundant, managed Apache ActiveMQ cluster within minutes.

Who would have thought I would have been so excited to revisit Apache ActiveMQ in 2017 after using SQS for many, many years? Choice is a wonderful thing.

Amazon GuardDuty
Maintaining application and information security in the modern world is increasingly complex and is constantly evolving and changing as new threats emerge. This is due to the scale, variety, and distribution of services required in a competitive online world.

At Amazon, security is our number one priority. Thus, we are always looking at how we can increase security detection and protection while simplifying the implementation of advanced security practices for our customers. As a result, we released Amazon GuardDuty, which provides intelligent threat detection by using a combination of multiple information sources, transactional telemetry, and the application of machine learning models developed by AWS. One of the biggest benefits of Amazon GuardDuty that I appreciate is that enabling this service requires zero software, agents, sensors, or network choke points. which can all impact performance or reliability of the service you are trying to protect. Amazon GuardDuty works by monitoring your VPC flow logs, AWS CloudTrail events, DNS logs, as well as combing other sources of security threats that AWS is aggregating from our own internal and external sources.

The use of machine learning in Amazon GuardDuty allows it to identify changes in behavior, which could be suspicious and require additional investigation. Amazon GuardDuty works across all of your AWS accounts allowing for an aggregated analysis and ensuring centralized management of detected threats across accounts. This is important for our larger customers who can be running many hundreds of AWS accounts across their organization, as providing a single common threat detection of their organizational use of AWS is critical to ensuring they are protecting themselves.

Detection, though, is only the beginning of what Amazon GuardDuty enables. When a threat is identified in Amazon GuardDuty, you can configure remediation scripts or trigger Lambda functions where you have custom responses that enable you to start building automated responses to a variety of different common threats. Speed of response is required when a security incident may be taking place. For example, Amazon GuardDuty detects that an Amazon Elastic Compute Cloud (Amazon EC2) instance might be compromised due to traffic from a known set of malicious IP addresses. Upon detection of a compromised EC2 instance, we could apply an access control entry restricting outbound traffic for that instance, which stops loss of data until a security engineer can assess what has occurred.

Whether you are a customer running a single service in a single account, or a global customer with hundreds of accounts with thousands of applications, or a startup with hundreds of micro-services with hourly release cycle in a devops world, I recommend enabling Amazon GuardDuty. We have a 30-day free trial available for all new customers of this service. As it is a monitor of events, there is no change required to your architecture within AWS.

Stay tuned for tomorrow’s post on AWS Media Services and Amazon Neptune.

 

Glenn during the Tour du Mont Blanc

Security updates for Wednesday

Post Syndicated from ris original https://lwn.net/Articles/740233/rss

Security updates have been issued by CentOS (apr and procmail), Debian (curl and xen), Fedora (cacti, git, jbig2dec, lucene4, mupdf, openssh, openssl, quagga, rpm, slurm, webkitgtk4, and xen), Oracle (apr and procmail), Red Hat (apr, java-1.7.1-ibm, java-1.8.0-ibm, procmail, samba4, and tcmu-runner), Scientific Linux (apr, procmail, and samba4), and Ubuntu (curl, openjdk-7, python2.7, and python3.4, python3.5).

Object models

Post Syndicated from Eevee original https://eev.ee/blog/2017/11/28/object-models/

Anonymous asks, with dollars:

More about programming languages!

Well then!

I’ve written before about what I think objects are: state and behavior, which in practice mostly means method calls.

I suspect that the popular impression of what objects are, and also how they should work, comes from whatever C++ and Java happen to do. From that point of view, the whole post above is probably nonsense. If the baseline notion of “object” is a rigid definition woven tightly into the design of two massively popular languages, then it doesn’t even make sense to talk about what “object” should mean — it does mean the features of those languages, and cannot possibly mean anything else.

I think that’s a shame! It piles a lot of baggage onto a fairly simple idea. Polymorphism, for example, has nothing to do with objects — it’s an escape hatch for static type systems. Inheritance isn’t the only way to reuse code between objects, but it’s the easiest and fastest one, so it’s what we get. Frankly, it’s much closer to a speed tradeoff than a fundamental part of the concept.

We could do with more experimentation around how objects work, but that’s impossible in the languages most commonly thought of as object-oriented.

Here, then, is a (very) brief run through the inner workings of objects in four very dynamic languages. I don’t think I really appreciated objects until I’d spent some time with Python, and I hope this can help someone else whet their own appetite.

Python 3

Of the four languages I’m going to touch on, Python will look the most familiar to the Java and C++ crowd. For starters, it actually has a class construct.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __neg__(self):
        return Vector(-self.x, -self.y)

    def __div__(self, denom):
        return Vector(self.x / denom, self.y / denom)

    @property
    def magnitude(self):
        return (self.x ** 2 + self.y ** 2) ** 0.5

    def normalized(self):
        return self / self.magnitude

The __init__ method is an initializer, which is like a constructor but named differently (because the object already exists in a usable form by the time the initializer is called). Operator overloading is done by implementing methods with other special __dunder__ names. Properties can be created with @property, where the @ is syntax for applying a wrapper function to a function as it’s defined. You can do inheritance, even multiply:

1
2
3
4
class Foo(A, B, C):
    def bar(self, x, y, z):
        # do some stuff
        super().bar(x, y, z)

Cool, a very traditional object model.

Except… for some details.

Some details

For one, Python objects don’t have a fixed layout. Code both inside and outside the class can add or remove whatever attributes they want from whatever object they want. The underlying storage is just a dict, Python’s mapping type. (Or, rather, something like one. Also, it’s possible to change, which will probably be the case for everything I say here.)

If you create some attributes at the class level, you’ll start to get a peek behind the curtains:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
class Foo:
    values = []

    def add_value(self, value):
        self.values.append(value)

a = Foo()
b = Foo()
a.add_value('a')
print(a.values)  # ['a']
b.add_value('b')
print(b.values)  # ['a', 'b']

The [] assigned to values isn’t a default assigned to each object. In fact, the individual objects don’t know about it at all! You can use vars(a) to get at the underlying storage dict, and you won’t see a values entry in there anywhere.

Instead, values lives on the class, which is a value (and thus an object) in its own right. When Python is asked for self.values, it checks to see if self has a values attribute; in this case, it doesn’t, so Python keeps going and asks the class for one.

Python’s object model is secretly prototypical — a class acts as a prototype, as a shared set of fallback values, for its objects.

In fact, this is also how method calls work! They aren’t syntactically special at all, which you can see by separating the attribute lookup from the call.

1
2
3
print("abc".startswith("a"))  # True
meth = "abc".startswith
print(meth("a"))  # True

Reading obj.method looks for a method attribute; if there isn’t one on obj, Python checks the class. Here, it finds one: it’s a function from the class body.

Ah, but wait! In the code I just showed, meth seems to “know” the object it came from, so it can’t just be a plain function. If you inspect the resulting value, it claims to be a “bound method” or “built-in method” rather than a function, too. Something funny is going on here, and that funny something is the descriptor protocol.

Descriptors

Python allows attributes to implement their own custom behavior when read from or written to. Such an attribute is called a descriptor. I’ve written about them before, but here’s a quick overview.

If Python looks up an attribute, finds it in a class, and the value it gets has a __get__ method… then instead of using that value, Python will use the return value of its __get__ method.

The @property decorator works this way. The magnitude property in my original example was shorthand for doing this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
class MagnitudeDescriptor:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return (instance.x ** 2 + instance.y ** 2) ** 0.5

class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    magnitude = MagnitudeDescriptor()

When you ask for somevec.magnitude, Python checks somevec but doesn’t find magnitude, so it consults the class instead. The class does have a magnitude, and it’s a value with a __get__ method, so Python calls that method and somevec.magnitude evaluates to its return value. (The instance is None check is because __get__ is called even if you get the descriptor directly from the class via Vector.magnitude. A descriptor intended to work on instances can’t do anything useful in that case, so the convention is to return the descriptor itself.)

You can also intercept attempts to write to or delete an attribute, and do absolutely whatever you want instead. But note that, similar to operating overloading in Python, the descriptor must be on a class; you can’t just slap one on an arbitrary object and have it work.

This brings me right around to how “bound methods” actually work. Functions are descriptors! The function type implements __get__, and when a function is retrieved from a class via an instance, that __get__ bundles the function and the instance together into a tiny bound method object. It’s essentially:

1
2
3
4
5
class FunctionType:
    def __get__(self, instance, owner):
        if instance is None:
            return self
        return functools.partial(self, instance)

The self passed as the first argument to methods is not special or magical in any way. It’s built out of a few simple pieces that are also readily accessible to Python code.

Note also that because obj.method() is just an attribute lookup and a call, Python doesn’t actually care whether method is a method on the class or just some callable thing on the object. You won’t get the auto-self behavior if it’s on the object, but otherwise there’s no difference.

More attribute access, and the interesting part

Descriptors are one of several ways to customize attribute access. Classes can implement __getattr__ to intervene when an attribute isn’t found on an object; __setattr__ and __delattr__ to intervene when any attribute is set or deleted; and __getattribute__ to implement unconditional attribute access. (That last one is a fantastic way to create accidental recursion, since any attribute access you do within __getattribute__ will of course call __getattribute__ again.)

Here’s what I really love about Python. It might seem like a magical special case that descriptors only work on classes, but it really isn’t. You could implement exactly the same behavior yourself, in pure Python, using only the things I’ve just told you about. Classes are themselves objects, remember, and they are instances of type, so the reason descriptors only work on classes is that type effectively does this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
class type:
    def __getattribute__(self, name):
        value = super().__getattribute__(name)
        # like all op overloads, __get__ must be on the type, not the instance
        ty = type(value)
        if hasattr(ty, '__get__'):
            # it's a descriptor!  this is a class access so there is no instance
            return ty.__get__(value, None, self)
        else:
            return value

You can even trivially prove to yourself that this is what’s going on by skipping over types behavior:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
class Descriptor:
    def __get__(self, instance, owner):
        print('called!')

class Foo:
    bar = Descriptor()

Foo.bar  # called!
type.__getattribute__(Foo, 'bar')  # called!
object.__getattribute__(Foo, 'bar')  # ...

And that’s not all! The mysterious super function, used to exhaustively traverse superclass method calls even in the face of diamond inheritance, can also be expressed in pure Python using these primitives. You could write your own superclass calling convention and use it exactly the same way as super.

This is one of the things I really like about Python. Very little of it is truly magical; virtually everything about the object model exists in the types rather than the language, which means virtually everything can be customized in pure Python.

Class creation and metaclasses

A very brief word on all of this stuff, since I could talk forever about Python and I have three other languages to get to.

The class block itself is fairly interesting. It looks like this:

1
2
class Name(*bases, **kwargs):
    # code

I’ve said several times that classes are objects, and in fact the class block is one big pile of syntactic sugar for calling type(...) with some arguments to create a new type object.

The Python documentation has a remarkably detailed description of this process, but the gist is:

  • Python determines the type of the new class — the metaclass — by looking for a metaclass keyword argument. If there isn’t one, Python uses the “lowest” type among the provided base classes. (If you’re not doing anything special, that’ll just be type, since every class inherits from object and object is an instance of type.)

  • Python executes the class body. It gets its own local scope, and any assignments or method definitions go into that scope.

  • Python now calls type(name, bases, attrs, **kwargs). The name is whatever was right after class; the bases are position arguments; and attrs is the class body’s local scope. (This is how methods and other class attributes end up on the class.) The brand new type is then assigned to Name.

Of course, you can mess with most of this. You can implement __prepare__ on a metaclass, for example, to use a custom mapping as storage for the local scope — including any reads, which allows for some interesting shenanigans. The only part you can’t really implement in pure Python is the scoping bit, which has a couple extra rules that make sense for classes. (In particular, functions defined within a class block don’t close over the class body; that would be nonsense.)

Object creation

Finally, there’s what actually happens when you create an object — including a class, which remember is just an invocation of type(...).

Calling Foo(...) is implemented as, well, a call. Any type can implement calls with the __call__ special method, and you’ll find that type itself does so. It looks something like this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
# oh, a fun wrinkle that's hard to express in pure python: type is a class, so
# it's an instance of itself
class type:
    def __call__(self, *args, **kwargs):
        # remember, here 'self' is a CLASS, an instance of type.
        # __new__ is a true constructor: object.__new__ allocates storage
        # for a new blank object
        instance = self.__new__(self, *args, **kwargs)
        # you can return whatever you want from __new__ (!), and __init__
        # is only called on it if it's of the right type
        if isinstance(instance, self):
            instance.__init__(*args, **kwargs)
        return instance

Again, you can trivially confirm this by asking any type for its __call__ method. Assuming that type doesn’t implement __call__ itself, you’ll get back a bound version of types implementation.

1
2
>>> list.__call__
<method-wrapper '__call__' of type object at 0x7fafb831a400>

You can thus implement __call__ in your own metaclass to completely change how subclasses are created — including skipping the creation altogether, if you like.

And… there’s a bunch of stuff I haven’t even touched on.

The Python philosophy

Python offers something that, on the surface, looks like a “traditional” class/object model. Under the hood, it acts more like a prototypical system, where failed attribute lookups simply defer to a superclass or metaclass.

The language also goes to almost superhuman lengths to expose all of its moving parts. Even the prototypical behavior is an implementation of __getattribute__ somewhere, which you are free to completely replace in your own types. Proxying and delegation are easy.

Also very nice is that these features “bundle” well, by which I mean a library author can do all manner of convoluted hijinks, and a consumer of that library doesn’t have to see any of it or understand how it works. You only need to inherit from a particular class (which has a metaclass), or use some descriptor as a decorator, or even learn any new syntax.

This meshes well with Python culture, which is pretty big on the principle of least surprise. These super-advanced features tend to be tightly confined to single simple features (like “makes a weak attribute“) or cordoned with DSLs (e.g., defining a form/struct/database table with a class body). In particular, I’ve never seen a metaclass in the wild implement its own __call__.

I have mixed feelings about that. It’s probably a good thing overall that the Python world shows such restraint, but I wonder if there are some very interesting possibilities we’re missing out on. I implemented a metaclass __call__ myself, just once, in an entity/component system that strove to minimize fuss when communicating between components. It never saw the light of day, but I enjoyed seeing some new things Python could do with the same relatively simple syntax. I wouldn’t mind seeing, say, an object model based on composition (with no inheritance) built atop Python’s primitives.

Lua

Lua doesn’t have an object model. Instead, it gives you a handful of very small primitives for building your own object model. This is pretty typical of Lua — it’s a very powerful language, but has been carefully constructed to be very small at the same time. I’ve never encountered anything else quite like it, and “but it starts indexing at 1!” really doesn’t do it justice.

The best way to demonstrate how objects work in Lua is to build some from scratch. We need two key features. The first is metatables, which bear a passing resemblance to Python’s metaclasses.

Tables and metatables

The table is Lua’s mapping type and its primary data structure. Keys can be any value other than nil. Lists are implemented as tables whose keys are consecutive integers starting from 1. Nothing terribly surprising. The dot operator is sugar for indexing with a string key.

1
2
3
4
5
local t = { a = 1, b = 2 }
print(t['a'])  -- 1
print(t.b)  -- 2
t.c = 3
print(t['c'])  -- 3

A metatable is a table that can be associated with another value (usually another table) to change its behavior. For example, operator overloading is implemented by assigning a function to a special key in a metatable.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
local t = { a = 1, b = 2 }
--print(t + 0)  -- error: attempt to perform arithmetic on a table value

local mt = {
    __add = function(left, right)
        return 12
    end,
}
setmetatable(t, mt)
print(t + 0)  -- 12

Now, the interesting part: one of the special keys is __index, which is consulted when the base table is indexed by a key it doesn’t contain. Here’s a table that claims every key maps to itself.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
local t = {}
local mt = {
    __index = function(table, key)
        return key
    end,
}
setmetatable(t, mt)
print(t.foo)  -- foo
print(t.bar)  -- bar
print(t[3])  -- 3

__index doesn’t have to be a function, either. It can be yet another table, in which case that table is simply indexed with the key. If the key still doesn’t exist and that table has a metatable with an __index, the process repeats.

With this, it’s easy to have several unrelated tables that act as a single table. Call the base table an object, fill the __index table with functions and call it a class, and you have half of an object system. You can even get prototypical inheritance by chaining __indexes together.

At this point things are a little confusing, since we have at least three tables going on, so here’s a diagram. Keep in mind that Lua doesn’t actually have anything called an “object”, “class”, or “method” — those are just convenient nicknames for a particular structure we might build with Lua’s primitives.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
                    ╔═══════════╗        ...
                    ║ metatable ║         ║
                    ╟───────────╢   ┌─────╨───────────────────────┐
                    ║ __index   ╫───┤ lookup table ("superclass") │
                    ╚═══╦═══════╝   ├─────────────────────────────┤
  ╔═══════════╗         ║           │ some other method           ┼─── function() ... end
  ║ metatable ║         ║           └─────────────────────────────┘
  ╟───────────╢   ┌─────╨──────────────────┐
  ║ __index   ╫───┤ lookup table ("class") │
  ╚═══╦═══════╝   ├────────────────────────┤
      ║           │ some method            ┼─── function() ... end
      ║           └────────────────────────┘
┌─────╨─────────────────┐
│ base table ("object") │
└───────────────────────┘

Note that a metatable is not the same as a class; it defines behavior, not methods. Conversely, if you try to use a class directly as a metatable, it will probably not do much. (This is pretty different from e.g. Python, where operator overloads are just methods with funny names. One nice thing about the Lua approach is that you can keep interface-like functionality separate from methods, and avoid clogging up arbitrary objects’ namespaces. You could even use a dummy table as a key and completely avoid name collisions.)

Anyway, code!

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
local class = {
    foo = function(a)
        print("foo got", a)
    end,
}
local mt = { __index = class }
-- setmetatable returns its first argument, so this is nice shorthand
local obj1 = setmetatable({}, mt)
local obj2 = setmetatable({}, mt)
obj1.foo(7)  -- foo got 7
obj2.foo(9)  -- foo got 9

Wait, wait, hang on. Didn’t I call these methods? How do they get at the object? Maybe Lua has a magical this variable?

Methods, sort of

Not quite, but this is where the other key feature comes in: method-call syntax. It’s the lightest touch of sugar, just enough to have method invocation.

1
2
3
4
5
6
7
8
9
-- note the colon!
a:b(c, d, ...)

-- exactly equivalent to this
-- (except that `a` is only evaluated once)
a.b(a, c, d, ...)

-- which of course is really this
a["b"](a, c, d, ...)

Now we can write methods that actually do something.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
local class = {
    bar = function(self)
        print("our score is", self.score)
    end,
}
local mt = { __index = class }
local obj1 = setmetatable({ score = 13 }, mt)
local obj2 = setmetatable({ score = 25 }, mt)
obj1:bar()  -- our score is 13
obj2:bar()  -- our score is 25

And that’s all you need. Much like Python, methods and data live in the same namespace, and Lua doesn’t care whether obj:method() finds a function on obj or gets one from the metatable’s __index. Unlike Python, the function will be passed self either way, because self comes from the use of : rather than from the lookup behavior.

(Aside: strictly speaking, any Lua value can have a metatable — and if you try to index a non-table, Lua will always consult the metatable’s __index. Strings all have the string library as a metatable, so you can call methods on them: try ("%s %s"):format(1, 2). I don’t think Lua lets user code set the metatable for non-tables, so this isn’t that interesting, but if you’re writing Lua bindings from C then you can wrap your pointers in metatables to give them methods implemented in C.)

Bringing it all together

Of course, writing all this stuff every time is a little tedious and error-prone, so instead you might want to wrap it all up inside a little function. No problem.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
local function make_object(body)
    -- create a metatable
    local mt = { __index = body }
    -- create a base table to serve as the object itself
    local obj = setmetatable({}, mt)
    -- and, done
    return obj
end

-- you can leave off parens if you're only passing in 
local Dog = {
    -- this acts as a "default" value; if obj.barks is missing, __index will
    -- kick in and find this value on the class.  but if obj.barks is assigned
    -- to, it'll go in the object and shadow the value here.
    barks = 0,

    bark = function(self)
        self.barks = self.barks + 1
        print("woof!")
    end,
}

local mydog = make_object(Dog)
mydog:bark()  -- woof!
mydog:bark()  -- woof!
mydog:bark()  -- woof!
print(mydog.barks)  -- 3
print(Dog.barks)  -- 0

It works, but it’s fairly barebones. The nice thing is that you can extend it pretty much however you want. I won’t reproduce an entire serious object system here — lord knows there are enough of them floating around — but the implementation I have for my LÖVE games lets me do this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
local Animal = Object:extend{
    cries = 0,
}

-- called automatically by Object
function Animal:init()
    print("whoops i couldn't think of anything interesting to put here")
end

-- this is just nice syntax for adding a first argument called 'self', then
-- assigning this function to Animal.cry
function Animal:cry()
    self.cries = self.cries + 1
end

local Cat = Animal:extend{}

function Cat:cry()
    print("meow!")
    Cat.__super.cry(self)
end

local cat = Cat()
cat:cry()  -- meow!
cat:cry()  -- meow!
print(cat.cries)  -- 2

When I say you can extend it however you want, I mean that. I could’ve implemented Python (2)-style super(Cat, self):cry() syntax; I just never got around to it. I could even make it work with multiple inheritance if I really wanted to — or I could go the complete opposite direction and only implement composition. I could implement descriptors, customizing the behavior of individual table keys. I could add pretty decent syntax for composition/proxying. I am trying very hard to end this section now.

The Lua philosophy

Lua’s philosophy is to… not have a philosophy? It gives you the bare minimum to make objects work, and you can do absolutely whatever you want from there. Lua does have something resembling prototypical inheritance, but it’s not so much a first-class feature as an emergent property of some very simple tools. And since you can make __index be a function, you could avoid the prototypical behavior and do something different entirely.

The very severe downside, of course, is that you have to find or build your own object system — which can get pretty confusing very quickly, what with the multiple small moving parts. Third-party code may also have its own object system with subtly different behavior. (Though, in my experience, third-party code tries very hard to avoid needing an object system at all.)

It’s hard to say what the Lua “culture” is like, since Lua is an embedded language that’s often a little different in each environment. I imagine it has a thousand millicultures, instead. I can say that the tedium of building my own object model has led me into something very “traditional”, with prototypical inheritance and whatnot. It’s partly what I’m used to, but it’s also just really dang easy to get working.

Likewise, while I love properties in Python and use them all the dang time, I’ve yet to use a single one in Lua. They wouldn’t be particularly hard to add to my object model, but having to add them myself (or shop around for an object model with them and also port all my code to use it) adds a huge amount of friction. I’ve thought about designing an interesting ECS with custom object behavior, too, but… is it really worth the effort? For all the power and flexibility Lua offers, the cost is that by the time I have something working at all, I’m too exhausted to actually use any of it.

JavaScript

JavaScript is notable for being preposterously heavily used, yet not having a class block.

Well. Okay. Yes. It has one now. It didn’t for a very long time, and even the one it has now is sugar.

Here’s a vector class again:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
class Vector {
    constructor(x, y) {
        this.x = x;
        this.y = y;
    }

    get magnitude() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    }

    dot(other) {
        return this.x * other.x + this.y * other.y;
    }
}

In “classic” JavaScript, this would be written as:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
function Vector(x, y) {
    this.x = x;
    this.y = y;
}

Object.defineProperty(Vector.prototype, 'magnitude', {
    configurable: true,
    enumerable: true,
    get: function() {
        return Math.sqrt(this.x * this.x + this.y * this.y);
    },
});


Vector.prototype.dot = function(other) {
    return this.x * other.x + this.y * other.y;
};

Hm, yes. I can see why they added class.

The JavaScript model

In JavaScript, a new type is defined in terms of a function, which is its constructor.

Right away we get into trouble here. There is a very big difference between these two invocations, which I actually completely forgot about just now after spending four hours writing about Python and Lua:

1
2
let vec = Vector(3, 4);
let vec = new Vector(3, 4);

The first calls the function Vector. It assigns some properties to this, which here is going to be window, so now you have a global x and y. It then returns nothing, so vec is undefined.

The second calls Vector with this set to a new empty object, then evaluates to that object. The result is what you’d actually expect.

(You can detect this situation with the strange new.target expression, but I have never once remembered to do so.)

From here, we have true, honest-to-god, first-class prototypical inheritance. The word “prototype” is even right there. When you write this:

1
vec.dot(vec2)

JavaScript will look for dot on vec and (presumably) not find it. It then consults vecs prototype, an object you can see for yourself by using Object.getPrototypeOf(). Since vec is a Vector, its prototype is Vector.prototype.

I stress that Vector.prototype is not the prototype for Vector. It’s the prototype for instances of Vector.

(I say “instance”, but the true type of vec here is still just object. If you want to find Vector, it’s automatically assigned to the constructor property of its own prototype, so it’s available as vec.constructor.)

Of course, Vector.prototype can itself have a prototype, in which case the process would continue if dot were not found. A common (and, arguably, very bad) way to simulate single inheritance is to set Class.prototype to an instance of a superclass to get the prototype right, then tack on the methods for Class. Nowadays we can do Object.create(Superclass.prototype).

Now that I’ve been through Python and Lua, though, this isn’t particularly surprising. I kinda spoiled it.

I suppose one difference in JavaScript is that you can tack arbitrary attributes directly onto Vector all you like, and they will remain invisible to instances since they aren’t in the prototype chain. This is kind of backwards from Lua, where you can squirrel stuff away in the metatable.

Another difference is that every single object in JavaScript has a bunch of properties already tacked on — the ones in Object.prototype. Every object (and by “object” I mean any mapping) has a prototype, and that prototype defaults to Object.prototype, and it has a bunch of ancient junk like isPrototypeOf.

(Nit: it’s possible to explicitly create an object with no prototype via Object.create(null).)

Like Lua, and unlike Python, JavaScript doesn’t distinguish between keys found on an object and keys found via a prototype. Properties can be defined on prototypes with Object.defineProperty(), but that works just as well directly on an object, too. JavaScript doesn’t have a lot of operator overloading, but some things like Symbol.iterator also work on both objects and prototypes.

About this

You may, at this point, be wondering what this is. Unlike Lua and Python (and the last language below), this is a special built-in value — a context value, invisibly passed for every function call.

It’s determined by where the function came from. If the function was the result of an attribute lookup, then this is set to the object containing that attribute. Otherwise, this is set to the global object, window. (You can also set this to whatever you want via the call method on functions.)

This decision is made lexically, i.e. from the literal source code as written. There are no Python-style bound methods. In other words:

1
2
3
4
5
// this = obj
obj.method()
// this = window
let meth = obj.method
meth()

Also, because this is reassigned on every function call, it cannot be meaningfully closed over, which makes using closures within methods incredibly annoying. The old approach was to assign this to some other regular name like self (which got syntax highlighting since it’s also a built-in name in browsers); then we got Function.bind, which produced a callable thing with a fixed context value, which was kind of nice; and now finally we have arrow functions, which explicitly close over the current this when they’re defined and don’t change it when called. Phew.

Class syntax

I already showed class syntax, and it’s really just one big macro for doing all the prototype stuff The Right Way. It even prevents you from calling the type without new. The underlying model is exactly the same, and you can inspect all the parts.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
class Vector { ... }

console.log(Vector.prototype);  // { dot: ..., magnitude: ..., ... }
let vec = new Vector(3, 4);
console.log(Object.getPrototypeOf(vec));  // same as Vector.prototype

// i don't know why you would subclass vector but let's roll with it
class Vectest extends Vector { ... }

console.log(Vectest.prototype);  // { ... }
console.log(Object.getPrototypeOf(Vectest.prototype))  // same as Vector.prototype

Alas, class syntax has a couple shortcomings. You can’t use the class block to assign arbitrary data to either the type object or the prototype — apparently it was deemed too confusing that mutations would be shared among instances. Which… is… how prototypes work. How Python works. How JavaScript itself, one of the most popular languages of all time, has worked for twenty-two years. Argh.

You can still do whatever assignment you want outside of the class block, of course. It’s just a little ugly, and not something I’d think to look for with a sugary class.

A more subtle result of this behavior is that a class block isn’t quite the same syntax as an object literal. The check for data isn’t a runtime thing; class Foo { x: 3 } fails to parse. So JavaScript now has two largely but not entirely identical styles of key/value block.

Attribute access

Here’s where things start to come apart at the seams, just a little bit.

JavaScript doesn’t really have an attribute protocol. Instead, it has two… extension points, I suppose.

One is Object.defineProperty, seen above. For common cases, there’s also the get syntax inside a property literal, which does the same thing. But unlike Python’s @property, these aren’t wrappers around some simple primitives; they are the primitives. JavaScript is the only language of these four to have “property that runs code on access” as a completely separate first-class concept.

If you want to intercept arbitrary attribute access (and some kinds of operators), there’s a completely different primitive: the Proxy type. It doesn’t let you intercept attribute access or operators; instead, it produces a wrapper object that supports interception and defers to the wrapped object by default.

It’s cool to see composition used in this way, but also, extremely weird. If you want to make your own type that overloads in or calling, you have to return a Proxy that wraps your own type, rather than actually returning your own type. And (unlike the other three languages in this post) you can’t return a different type from a constructor, so you have to throw that away and produce objects only from a factory. And instanceof would be broken, but you can at least fix that with Symbol.hasInstance — which is really operator overloading, implement yet another completely different way.

I know the design here is a result of legacy and speed — if any object could intercept all attribute access, then all attribute access would be slowed down everywhere. Fair enough. It still leaves the surface area of the language a bit… bumpy?

The JavaScript philosophy

It’s a little hard to tell. The original idea of prototypes was interesting, but it was hidden behind some very awkward syntax. Since then, we’ve gotten a bunch of extra features awkwardly bolted on to reflect the wildly varied things the built-in types and DOM API were already doing. We have class syntax, but it’s been explicitly designed to avoid exposing the prototype parts of the model.

I admit I don’t do a lot of heavy JavaScript, so I might just be overlooking it, but I’ve seen virtually no code that makes use of any of the recent advances in object capabilities. Forget about custom iterators or overloading call; I can’t remember seeing any JavaScript in the wild that even uses properties yet. I don’t know if everyone’s waiting for sufficient browser support, nobody knows about them, or nobody cares.

The model has advanced recently, but I suspect JavaScript is still shackled to its legacy of “something about prototypes, I don’t really get it, just copy the other code that’s there” as an object model. Alas! Prototypes are so good. Hopefully class syntax will make it a bit more accessible, as it has in Python.

Perl 5

Perl 5 also doesn’t have an object system and expects you to build your own. But where Lua gives you two simple, powerful tools for building one, Perl 5 feels more like a puzzle with half the pieces missing. Clearly they were going for something, but they only gave you half of it.

In brief, a Perl object is a reference that has been blessed with a package.

I need to explain a few things. Honestly, one of the biggest problems with the original Perl object setup was how many strange corners and unique jargon you had to understand just to get off the ground.

(If you want to try running any of this code, you should stick a use v5.26; as the first line. Perl is very big on backwards compatibility, so you need to opt into breaking changes, and even the mundane say builtin is behind a feature gate.)

References

A reference in Perl is sort of like a pointer, but its main use is very different. See, Perl has the strange property that its data structures try very hard to spill their contents all over the place. Despite having dedicated syntax for arrays — @foo is an array variable, distinct from the single scalar variable $foo — it’s actually impossible to nest arrays.

1
2
3
my @foo = (1, 2, 3, 4);
my @bar = (@foo, @foo);
# @bar is now a flat list of eight items: 1, 2, 3, 4, 1, 2, 3, 4

The idea, I guess, is that an array is not one thing. It’s not a container, which happens to hold multiple things; it is multiple things. Anywhere that expects a single value, such as an array element, cannot contain an array, because an array fundamentally is not a single value.

And so we have “references”, which are a form of indirection, but also have the nice property that they’re single values. They add containment around arrays, and in general they make working with most of Perl’s primitive types much more sensible. A reference to a variable can be taken with the \ operator, or you can use [ ... ] and { ... } to directly create references to anonymous arrays or hashes.

1
2
3
my @foo = (1, 2, 3, 4);
my @bar = (\@foo, \@foo);
# @bar is now a nested list of two items: [1, 2, 3, 4], [1, 2, 3, 4]

(Incidentally, this is the sole reason I initially abandoned Perl for Python. Non-trivial software kinda requires nesting a lot of data structures, so you end up with references everywhere, and the syntax for going back and forth between a reference and its contents is tedious and ugly.)

A Perl object must be a reference. Perl doesn’t care what kind of reference — it’s usually a hash reference, since hashes are a convenient place to store arbitrary properties, but it could just as well be a reference to an array, a scalar, or even a sub (i.e. function) or filehandle.

I’m getting a little ahead of myself. First, the other half: blessing and packages.

Packages and blessing

Perl packages are just namespaces. A package looks like this:

1
2
3
4
5
6
7
package Foo::Bar;

sub quux {
    say "hi from quux!";
}

# now Foo::Bar::quux() can be called from anywhere

Nothing shocking, right? It’s just a named container. A lot of the details are kind of weird, like how a package exists in some liminal quasi-value space, but the basic idea is a Bag Of Stuff.

The final piece is “blessing,” which is Perl’s funny name for binding a package to a reference. A very basic class might look like this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
package Vector;

# the name 'new' is convention, not special
sub new {
    # perl argument passing is weird, don't ask
    my ($class, $x, $y) = @_;

    # create the object itself -- here, unusually, an array reference makes sense
    my $self = [ $x, $y ];

    # associate the package with that reference
    # note that $class here is just the regular string, 'Vector'
    bless $self, $class;

    return $self;
}

sub x {
    my ($self) = @_;
    return $self->[0];
}

sub y {
    my ($self) = @_;
    return $self->[1];
}

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

# switch back to the "default" package
package main;

# -> is method call syntax, which passes the invocant as the first argument;
# for a package, that's just the package name
my $vec = Vector->new(3, 4);
say $vec->magnitude;  # 5

A few things of note here. First, $self->[0] has nothing to do with objects; it’s normal syntax for getting the value of a index 0 out of an array reference called $self. (Most classes are based on hashrefs and would use $self->{value} instead.) A blessed reference is still a reference and can be treated like one.

In general, -> is Perl’s dereferencey operator, but its exact behavior depends on what follows. If it’s followed by brackets, then it’ll apply the brackets to the thing in the reference: ->{} to index a hash reference, ->[] to index an array reference, and ->() to call a function reference.

But if -> is followed by an identifier, then it’s a method call. For packages, that means calling a function in the package and passing the package name as the first argument. For objects — blessed references — that means calling a function in the associated package and passing the object as the first argument.

This is a little weird! A blessed reference is a superposition of two things: its normal reference behavior, and some completely orthogonal object behavior. Also, object behavior has no notion of methods vs data; it only knows about methods. Perl lets you omit parentheses in a lot of places, including when calling a method with no arguments, so $vec->magnitude is really $vec->magnitude().

Perl’s blessing bears some similarities to Lua’s metatables, but ultimately Perl is much closer to Ruby’s “message passing” approach than the above three languages’ approaches of “get me something and maybe it’ll be callable”. (But this is no surprise — Ruby is a spiritual successor to Perl 5.)

All of this leads to one little wrinkle: how do you actually expose data? Above, I had to write x and y methods. Am I supposed to do that for every single attribute on my type?

Yes! But don’t worry, there are third-party modules to help with this incredibly fundamental task. Take Class::Accessor::Fast, so named because it’s faster than Class::Accessor:

1
2
3
package Foo;
use base qw(Class::Accessor::Fast);
__PACKAGE__->mk_accessors(qw(fred wilma barney));

(__PACKAGE__ is the lexical name of the current package; qw(...) is a list literal that splits its contents on whitespace.)

This assumes you’re using a hashref with keys of the same names as the attributes. $obj->fred will return the fred key from your hashref, and $obj->fred(4) will change it to 4.

You also, somewhat bizarrely, have to inherit from Class::Accessor::Fast. Speaking of which,

Inheritance

Inheritance is done by populating the package-global @ISA array with some number of (string) names of parent packages. Most code instead opts to write use base ...;, which does the same thing. Or, more commonly, use parent ...;, which… also… does the same thing.

Every package implicitly inherits from UNIVERSAL, which can be freely modified by Perl code.

A method can call its superclass method with the SUPER:: pseudo-package:

1
2
3
4
sub foo {
    my ($self) = @_;
    $self->SUPER::foo;
}

However, this does a depth-first search, which means it almost certainly does the wrong thing when faced with multiple inheritance. For a while the accepted solution involved a third-party module, but Perl eventually grew an alternative you have to opt into: C3, which may be more familiar to you as the order Python uses.

1
2
3
4
5
6
use mro 'c3';

sub foo {
    my ($self) = @_;
    $self->next::method;
}

Offhand, I’m not actually sure how next::method works, seeing as it was originally implemented in pure Perl code. I suspect it involves peeking at the caller’s stack frame. If so, then this is a very different style of customizability from e.g. Python — the MRO was never intended to be pluggable, and the use of a special pseudo-package means it isn’t really, but someone was determined enough to make it happen anyway.

Operator overloading and whatnot

Operator overloading looks a little weird, though really it’s pretty standard Perl.

1
2
3
4
5
6
7
8
package MyClass;

use overload '+' => \&_add;

sub _add {
    my ($self, $other, $swap) = @_;
    ...
}

use overload here is a pragma, where “pragma” means “regular-ass module that does some wizardry when imported”.

\&_add is how you get a reference to the _add sub so you can pass it to the overload module. If you just said &_add or _add, that would call it.

And that’s it; you just pass a map of operators to functions to this built-in module. No worry about name clashes or pollution, which is pretty nice. You don’t even have to give references to functions that live in the package, if you don’t want them to clog your namespace; you could put them in another package, or even inline them anonymously.

One especially interesting thing is that Perl lets you overload every operator. Perl has a lot of operators. It considers some math builtins like sqrt and trig functions to be operators, or at least operator-y enough that you can overload them. You can also overload the “file text” operators, such as -e $path to test whether a file exists. You can overload conversions, including implicit conversion to a regex. And most fascinating to me, you can overload dereferencing — that is, the thing Perl does when you say $hashref->{key} to get at the underlying hash. So a single object could pretend to be references of multiple different types, including a subref to implement callability. Neat.

Somewhat related: you can overload basic operators (indexing, etc.) on basic types (not references!) with the tie function, which is designed completely differently and looks for methods with fixed names. Go figure.

You can intercept calls to nonexistent methods by implementing a function called AUTOLOAD, within which the $AUTOLOAD global will contain the name of the method being called. Originally this feature was, I think, intended for loading binary components or large libraries on-the-fly only when needed, hence the name. Offhand I’m not sure I ever saw it used the way __getattr__ is used in Python.

Is there a way to intercept all method calls? I don’t think so, but it is Perl, so I must be forgetting something.

Actually no one does this any more

Like a decade ago, a council of elder sages sat down and put together a whole whizbang system that covers all of it: Moose.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
package Vector;
use Moose;

has x => (is => 'rw', isa => 'Int');
has y => (is => 'rw', isa => 'Int');

sub magnitude {
    my ($self) = @_;
    return sqrt($self->x ** 2 + $self->y ** 2);
}

Moose has its own way to do pretty much everything, and it’s all built on the same primitives. Moose also adds metaclasses, somehow, despite that the underlying model doesn’t actually support them? I’m not entirely sure how they managed that, but I do remember doing some class introspection with Moose and it was much nicer than the built-in way.

(If you’re wondering, the built-in way begins with looking at the hash called %Vector::. No, that’s not a typo.)

I really cannot stress enough just how much stuff Moose does, but I don’t want to delve into it here since Moose itself is not actually the language model.

The Perl philosophy

I hope you can see what I meant with what I first said about Perl, now. It has multiple inheritance with an MRO, but uses the wrong one by default. It has extensive operator overloading, which looks nothing like how inheritance works, and also some of it uses a totally different mechanism with special method names instead. It only understands methods, not data, leaving you to figure out accessors by hand.

There’s 70% of an object system here with a clear general design it was gunning for, but none of the pieces really look anything like each other. It’s weird, in a distinctly Perl way.

The result is certainly flexible, at least! It’s especially cool that you can use whatever kind of reference you want for storage, though even as I say that, I acknowledge it’s no different from simply subclassing list or something in Python. It feels different in Perl, but maybe only because it looks so different.

I haven’t written much Perl in a long time, so I don’t know what the community is like any more. Moose was already ubiquitous when I left, which you’d think would let me say “the community mostly focuses on the stuff Moose can do” — but even a decade ago, Moose could already do far more than I had ever seen done by hand in Perl. It’s always made a big deal out of roles (read: interfaces), for instance, despite that I’d never seen anyone care about them in Perl before Moose came along. Maybe their presence in Moose has made them more popular? Who knows.

Also, I wrote Perl seriously, but in the intervening years I’ve only encountered people who only ever used Perl for one-offs. Maybe it’ll come as a surprise to a lot of readers that Perl has an object model at all.

End

Well, that was fun! I hope any of that made sense.

Special mention goes to Rust, which doesn’t have an object model you can fiddle with at runtime, but does do things a little differently.

It’s been really interesting thinking about how tiny differences make a huge impact on what people do in practice. Take the choice of storage in Perl versus Python. Perl’s massively common URI class uses a string as the storage, nothing else; I haven’t seen anything like that in Python aside from markupsafe, which is specifically designed as a string type. I would guess this is partly because Perl makes you choose — using a hashref is an obvious default, but you have to make that choice one way or the other. In Python (especially 3), inheriting from object and getting dict-based storage is the obvious thing to do; the ability to use another type isn’t quite so obvious, and doing it “right” involves a tiny bit of extra work.

Or, consider that Lua could have descriptors, but the extra bit of work (especially design work) has been enough of an impediment that I’ve never implemented them. I don’t think the object implementations I’ve looked at have included them, either. Super weird!

In that light, it’s only natural that objects would be so strongly associated with the features Java and C++ attach to them. I think that makes it all the more important to play around! Look at what Moose has done. No, really, you should bear in mind my description of how Perl does stuff and flip through the Moose documentation. It’s amazing what they’ve built.

Introducing AWS AppSync – Build data-driven apps with real-time and off-line capabilities

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/introducing-amazon-appsync/

In this day and age, it is almost impossible to do without our mobile devices and the applications that help make our lives easier. As our dependency on our mobile phone grows, the mobile application market has exploded with millions of apps vying for our attention. For mobile developers, this means that we must ensure that we build applications that provide the quality, real-time experiences that app users desire.  Therefore, it has become essential that mobile applications are developed to include features such as multi-user data synchronization, offline network support, and data discovery, just to name a few.  According to several articles, I read recently about mobile development trends on publications like InfoQ, DZone, and the mobile development blog AlleviateTech, one of the key elements in of delivering the aforementioned capabilities is with cloud-driven mobile applications.  It seems that this is especially true, as it related to mobile data synchronization and data storage.

That being the case, it is a perfect time for me to announce a new service for building innovative mobile applications that are driven by data-intensive services in the cloud; AWS AppSync. AWS AppSync is a fully managed serverless GraphQL service for real-time data queries, synchronization, communications and offline programming features. For those not familiar, let me briefly share some information about the open GraphQL specification. GraphQL is a responsive data query language and server-side runtime for querying data sources that allow for real-time data retrieval and dynamic query execution. You can use GraphQL to build a responsive API for use in when building client applications. GraphQL works at the application layer and provides a type system for defining schemas. These schemas serve as specifications to define how operations should be performed on the data and how the data should be structured when retrieved. Additionally, GraphQL has a declarative coding model which is supported by many client libraries and frameworks including React, React Native, iOS, and Android.

Now the power of the GraphQL open standard query language is being brought to you in a rich managed service with AWS AppSync.  With AppSync developers can simplify the retrieval and manipulation of data across multiple data sources with ease, allowing them to quickly prototype, build and create robust, collaborative, multi-user applications. AppSync keeps data updated when devices are connected, but enables developers to build solutions that work offline by caching data locally and synchronizing local data when connections become available.

Let’s discuss some key concepts of AWS AppSync and how the service works.

AppSync Concepts

  • AWS AppSync Client: service client that defines operations, wraps authorization details of requests, and manage offline logic.
  • Data Source: the data storage system or a trigger housing data
  • Identity: a set of credentials with permissions and identification context provided with requests to GraphQL proxy
  • GraphQL Proxy: the GraphQL engine component for processing and mapping requests, handling conflict resolution, and managing Fine Grained Access Control
  • Operation: one of three GraphQL operations supported in AppSync
    • Query: a read-only fetch call to the data
    • Mutation: a write of the data followed by a fetch,
    • Subscription: long-lived connections that receive data in response to events.
  • Action: a notification to connected subscribers from a GraphQL subscription.
  • Resolver: function using request and response mapping templates that converts and executes payload against data source

How It Works

A schema is created to define types and capabilities of the desired GraphQL API and tied to a Resolver function.  The schema can be created to mirror existing data sources or AWS AppSync can create tables automatically based the schema definition. Developers can also use GraphQL features for data discovery without having knowledge of the backend data sources. After a schema definition is established, an AWS AppSync client can be configured with an operation request, like a Query operation. The client submits the operation request to GraphQL Proxy along with an identity context and credentials. The GraphQL Proxy passes this request to the Resolver which maps and executes the request payload against pre-configured AWS data services like an Amazon DynamoDB table, an AWS Lambda function, or a search capability using Amazon Elasticsearch. The Resolver executes calls to one or all of these services within a single network call minimizing CPU cycles and bandwidth needs and returns the response to the client. Additionally, the client application can change data requirements in code on demand and the AppSync GraphQL API will dynamically map requests for data accordingly, allowing prototyping and faster development.

In order to take a quick peek at the service, I’ll go to the AWS AppSync console. I’ll click the Create API button to get started.

 

When the Create new API screen opens, I’ll give my new API a name, TarasTestApp, and since I am just exploring the new service I will select the Sample schema option.  You may notice from the informational dialog box on the screen that in using the sample schema, AWS AppSync will automatically create the DynamoDB tables and the IAM roles for me.It will also deploy the TarasTestApp API on my behalf.  After review of the sample schema provided by the console, I’ll click the Create button to create my test API.

After the TaraTestApp API has been created and the associated AWS resources provisioned on my behalf, I can make updates to the schema, data source, or connect my data source(s) to a resolver. I also can integrate my GraphQL API into an iOS, Android, Web, or React Native application by cloning the sample repo from GitHub and downloading the accompanying GraphQL schema.  These application samples are great to help get you started and they are pre-configured to function in offline scenarios.

If I select the Schema menu option on the console, I can update and view the TarasTestApp GraphQL API schema.


Additionally, if I select the Data Sources menu option in the console, I can see the existing data sources.  Within this screen, I can update, delete, or add data sources if I so desire.

Next, I will select the Query menu option which takes me to the console tool for writing and testing queries. Since I chose the sample schema and the AWS AppSync service did most of the heavy lifting for me, I’ll try a query against my new GraphQL API.

I’ll use a mutation to add data for the event type in my schema. Since this is a mutation and it first writes data and then does a read of the data, I want the query to return values for name and where.

If I go to the DynamoDB table created for the event type in the schema, I will see that the values from my query have been successfully written into the table. Now that was a pretty simple task to write and retrieve data based on a GraphQL API schema from a data source, don’t you think.


 Summary

AWS AppSync is currently in AWS AppSync is in Public Preview and you can sign up today. It supports development for iOS, Android, and JavaScript applications. You can take advantage of this managed GraphQL service by going to the AWS AppSync console or learn more by reviewing more details about the service by reading a tutorial in the AWS documentation for the service or checking out our AWS AppSync Developer Guide.

Tara

 

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/740133/rss

Security updates have been issued by Arch Linux (powerdns and powerdns-recursor), CentOS (curl and samba), Debian (ffmpeg and roundcube), Fedora (cacti and samba), openSUSE (thunderbird), Oracle (curl), Red Hat (java-1.8.0-ibm and rh-mysql56-mysql), Scientific Linux (curl), Slackware (samba), SUSE (kernel-firmware and samba), and Ubuntu (exim4, firefox, libxml-libxml-perl, optipng, and postgresql-common).

Using Amazon Redshift Spectrum, Amazon Athena, and AWS Glue with Node.js in Production

Post Syndicated from Rafi Ton original https://aws.amazon.com/blogs/big-data/using-amazon-redshift-spectrum-amazon-athena-and-aws-glue-with-node-js-in-production/

This is a guest post by Rafi Ton, founder and CEO of NUVIAD. NUVIAD is, in their own words, “a mobile marketing platform providing professional marketers, agencies and local businesses state of the art tools to promote their products and services through hyper targeting, big data analytics and advanced machine learning tools.”

At NUVIAD, we’ve been using Amazon Redshift as our main data warehouse solution for more than 3 years.

We store massive amounts of ad transaction data that our users and partners analyze to determine ad campaign strategies. When running real-time bidding (RTB) campaigns in large scale, data freshness is critical so that our users can respond rapidly to changes in campaign performance. We chose Amazon Redshift because of its simplicity, scalability, performance, and ability to load new data in near real time.

Over the past three years, our customer base grew significantly and so did our data. We saw our Amazon Redshift cluster grow from three nodes to 65 nodes. To balance cost and analytics performance, we looked for a way to store large amounts of less-frequently analyzed data at a lower cost. Yet, we still wanted to have the data immediately available for user queries and to meet their expectations for fast performance. We turned to Amazon Redshift Spectrum.

In this post, I explain the reasons why we extended Amazon Redshift with Redshift Spectrum as our modern data warehouse. I cover how our data growth and the need to balance cost and performance led us to adopt Redshift Spectrum. I also share key performance metrics in our environment, and discuss the additional AWS services that provide a scalable and fast environment, with data available for immediate querying by our growing user base.

Amazon Redshift as our foundation

The ability to provide fresh, up-to-the-minute data to our customers and partners was always a main goal with our platform. We saw other solutions provide data that was a few hours old, but this was not good enough for us. We insisted on providing the freshest data possible. For us, that meant loading Amazon Redshift in frequent micro batches and allowing our customers to query Amazon Redshift directly to get results in near real time.

The benefits were immediately evident. Our customers could see how their campaigns performed faster than with other solutions, and react sooner to the ever-changing media supply pricing and availability. They were very happy.

However, this approach required Amazon Redshift to store a lot of data for long periods, and our data grew substantially. In our peak, we maintained a cluster running 65 DC1.large nodes. The impact on our Amazon Redshift cluster was evident, and we saw our CPU utilization grow to 90%.

Why we extended Amazon Redshift to Redshift Spectrum

Redshift Spectrum gives us the ability to run SQL queries using the powerful Amazon Redshift query engine against data stored in Amazon S3, without needing to load the data. With Redshift Spectrum, we store data where we want, at the cost that we want. We have the data available for analytics when our users need it with the performance they expect.

Seamless scalability, high performance, and unlimited concurrency

Scaling Redshift Spectrum is a simple process. First, it allows us to leverage Amazon S3 as the storage engine and get practically unlimited data capacity.

Second, if we need more compute power, we can leverage Redshift Spectrum’s distributed compute engine over thousands of nodes to provide superior performance – perfect for complex queries running against massive amounts of data.

Third, all Redshift Spectrum clusters access the same data catalog so that we don’t have to worry about data migration at all, making scaling effortless and seamless.

Lastly, since Redshift Spectrum distributes queries across potentially thousands of nodes, they are not affected by other queries, providing much more stable performance and unlimited concurrency.

Keeping it SQL

Redshift Spectrum uses the same query engine as Amazon Redshift. This means that we did not need to change our BI tools or query syntax, whether we used complex queries across a single table or joins across multiple tables.

An interesting capability introduced recently is the ability to create a view that spans both Amazon Redshift and Redshift Spectrum external tables. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view.

Leveraging Parquet for higher performance

Parquet is a columnar data format that provides superior performance and allows Redshift Spectrum (or Amazon Athena) to scan significantly less data. With less I/O, queries run faster and we pay less per query. You can read all about Parquet at https://parquet.apache.org/ or https://en.wikipedia.org/wiki/Apache_Parquet.

Lower cost

From a cost perspective, we pay standard rates for our data in Amazon S3, and only small amounts per query to analyze data with Redshift Spectrum. Using the Parquet format, we can significantly reduce the amount of data scanned. Our costs are now lower, and our users get fast results even for large complex queries.

What we learned about Amazon Redshift vs. Redshift Spectrum performance

When we first started looking at Redshift Spectrum, we wanted to put it to the test. We wanted to know how it would compare to Amazon Redshift, so we looked at two key questions:

  1. What is the performance difference between Amazon Redshift and Redshift Spectrum on simple and complex queries?
  2. Does the data format impact performance?

During the migration phase, we had our dataset stored in Amazon Redshift and S3 as CSV/GZIP and as Parquet file formats. We tested three configurations:

  • Amazon Redshift cluster with 28 DC1.large nodes
  • Redshift Spectrum using CSV/GZIP
  • Redshift Spectrum using Parquet

We performed benchmarks for simple and complex queries on one month’s worth of data. We tested how much time it took to perform the query, and how consistent the results were when running the same query multiple times. The data we used for the tests was already partitioned by date and hour. Properly partitioning the data improves performance significantly and reduces query times.

Simple query

First, we tested a simple query aggregating billing data across a month:

SELECT 
  user_id, 
  count(*) AS impressions, 
  SUM(billing)::decimal /1000000 AS billing 
FROM <table_name> 
WHERE 
  date >= '2017-08-01' AND 
  date <= '2017-08-31'  
GROUP BY 
  user_id;

We ran the same query seven times and measured the response times (red marking the longest time and green the shortest time):

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum
CSV
Redshift Spectrum Parquet
Run #1 39.65 45.11 11.92
Run #2 15.26 43.13 12.05
Run #3 15.27 46.47 13.38
Run #4 21.22 51.02 12.74
Run #5 17.27 43.35 11.76
Run #6 16.67 44.23 13.67
Run #7 25.37 40.39 12.75
Average 21.53  44.82 12.61

For simple queries, Amazon Redshift performed better than Redshift Spectrum, as we thought, because the data is local to Amazon Redshift.

What was surprising was that using Parquet data format in Redshift Spectrum significantly beat ‘traditional’ Amazon Redshift performance. For our queries, using Parquet data format with Redshift Spectrum delivered an average 40% performance gain over traditional Amazon Redshift. Furthermore, Redshift Spectrum showed high consistency in execution time with a smaller difference between the slowest run and the fastest run.

Comparing the amount of data scanned when using CSV/GZIP and Parquet, the difference was also significant:

Data Scanned (GB)
CSV (Gzip) 135.49
Parquet 2.83

Because we pay only for the data scanned by Redshift Spectrum, the cost saving of using Parquet is evident and substantial.

Complex query

Next, we compared the same three configurations with a complex query.

Execution Time (seconds)
  Amazon Redshift Redshift Spectrum CSV Redshift Spectrum Parquet
Run #1 329.80 84.20 42.40
Run #2 167.60 65.30 35.10
Run #3 165.20 62.20 23.90
Run #4 273.90 74.90 55.90
Run #5 167.70 69.00 58.40
Average 220.84 71.12 43.14

This time, Redshift Spectrum using Parquet cut the average query time by 80% compared to traditional Amazon Redshift!

Bottom line: For complex queries, Redshift Spectrum provided a 67% performance gain over Amazon Redshift. Using the Parquet data format, Redshift Spectrum delivered an 80% performance improvement over Amazon Redshift. For us, this was substantial.

Optimizing the data structure for different workloads

Because the cost of S3 is relatively inexpensive and we pay only for the data scanned by each query, we believe that it makes sense to keep our data in different formats for different workloads and different analytics engines. It is important to note that we can have any number of tables pointing to the same data on S3. It all depends on how we partition the data and update the table partitions.

Data permutations

For example, we have a process that runs every minute and generates statistics for the last minute of data collected. With Amazon Redshift, this would be done by running the query on the table with something as follows:

SELECT 
  user, 
  COUNT(*) 
FROM 
  events_table 
WHERE 
  ts BETWEEN ‘2017-08-01 14:00:00’ AND ‘2017-08-01 14:00:59’ 
GROUP BY 
  user;

(Assuming ‘ts’ is your column storing the time stamp for each event.)

With Redshift Spectrum, we pay for the data scanned in each query. If the data is partitioned by the minute instead of the hour, a query looking at one minute would be 1/60th the cost. If we use a temporary table that points only to the data of the last minute, we save that unnecessary cost.

Creating Parquet data efficiently

On the average, we have 800 instances that process our traffic. Each instance sends events that are eventually loaded into Amazon Redshift. When we started three years ago, we would offload data from each server to S3 and then perform a periodic copy command from S3 to Amazon Redshift.

Recently, Amazon Kinesis Firehose added the capability to offload data directly to Amazon Redshift. While this is now a viable option, we kept the same collection process that worked flawlessly and efficiently for three years.

This changed, however, when we incorporated Redshift Spectrum. With Redshift Spectrum, we needed to find a way to:

  • Collect the event data from the instances.
  • Save the data in Parquet format.
  • Partition the data effectively.

To accomplish this, we save the data as CSV and then transform it to Parquet. The most effective method to generate the Parquet files is to:

  1. Send the data in one-minute intervals from the instances to Kinesis Firehose with an S3 temporary bucket as the destination.
  2. Aggregate hourly data and convert it to Parquet using AWS Lambda and AWS Glue.
  3. Add the Parquet data to S3 by updating the table partitions.

With this new process, we had to give more attention to validating the data before we sent it to Kinesis Firehose, because a single corrupted record in a partition fails queries on that partition.

Data validation

To store our click data in a table, we considered the following SQL create table command:

create external TABLE spectrum.blog_clicks (
    user_id varchar(50),
    campaign_id varchar(50),
    os varchar(50),
    ua varchar(255),
    ts bigint,
    billing float
)
partitioned by (date date, hour smallint)  
stored as parquet
location 's3://nuviad-temp/blog/clicks/';

The above statement defines a new external table (all Redshift Spectrum tables are external tables) with a few attributes. We stored ‘ts’ as a Unix time stamp and not as Timestamp, and billing data is stored as float and not decimal (more on that later). We also said that the data is partitioned by date and hour, and then stored as Parquet on S3.

First, we need to get the table definitions. This can be achieved by running the following query:

SELECT 
  * 
FROM 
  svv_external_columns 
WHERE 
  tablename = 'blog_clicks';

This query lists all the columns in the table with their respective definitions:

schemaname tablename columnname external_type columnnum part_key
spectrum blog_clicks user_id varchar(50) 1 0
spectrum blog_clicks campaign_id varchar(50) 2 0
spectrum blog_clicks os varchar(50) 3 0
spectrum blog_clicks ua varchar(255) 4 0
spectrum blog_clicks ts bigint 5 0
spectrum blog_clicks billing double 6 0
spectrum blog_clicks date date 7 1
spectrum blog_clicks hour smallint 8 2

Now we can use this data to create a validation schema for our data:

const rtb_request_schema = {
    "name": "clicks",
    "items": {
        "user_id": {
            "type": "string",
            "max_length": 100
        },
        "campaign_id": {
            "type": "string",
            "max_length": 50
        },
        "os": {
            "type": "string",
            "max_length": 50            
        },
        "ua": {
            "type": "string",
            "max_length": 255            
        },
        "ts": {
            "type": "integer",
            "min_value": 0,
            "max_value": 9999999999999
        },
        "billing": {
            "type": "float",
            "min_value": 0,
            "max_value": 9999999999999
        }
    }
};

Next, we create a function that uses this schema to validate data:

function valueIsValid(value, item_schema) {
    if (schema.type == 'string') {
        return (typeof value == 'string' && value.length <= schema.max_length);
    }
    else if (schema.type == 'integer') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'float' || schema.type == 'double') {
        return (typeof value == 'number' && value >= schema.min_value && value <= schema.max_value);
    }
    else if (schema.type == 'boolean') {
        return typeof value == 'boolean';
    }
    else if (schema.type == 'timestamp') {
        return (new Date(value)).getTime() > 0;
    }
    else {
        return true;
    }
}

Near real-time data loading with Kinesis Firehose

On Kinesis Firehose, we created a new delivery stream to handle the events as follows:

Delivery stream name: events
Source: Direct PUT
S3 bucket: nuviad-events
S3 prefix: rtb/
IAM role: firehose_delivery_role_1
Data transformation: Disabled
Source record backup: Disabled
S3 buffer size (MB): 100
S3 buffer interval (sec): 60
S3 Compression: GZIP
S3 Encryption: No Encryption
Status: ACTIVE
Error logging: Enabled

This delivery stream aggregates event data every minute, or up to 100 MB, and writes the data to an S3 bucket as a CSV/GZIP compressed file. Next, after we have the data validated, we can safely send it to our Kinesis Firehose API:

if (validated) {
    let itemString = item.join('|')+'\n'; //Sending csv delimited by pipe and adding new line

    let params = {
        DeliveryStreamName: 'events',
        Record: {
            Data: itemString
        }
    };

    firehose.putRecord(params, function(err, data) {
        if (err) {
            console.error(err, err.stack);        
        }
        else {
            // Continue to your next step 
        }
    });
}

Now, we have a single CSV file representing one minute of event data stored in S3. The files are named automatically by Kinesis Firehose by adding a UTC time prefix in the format YYYY/MM/DD/HH before writing objects to S3. Because we use the date and hour as partitions, we need to change the file naming and location to fit our Redshift Spectrum schema.

Automating data distribution using AWS Lambda

We created a simple Lambda function triggered by an S3 put event that copies the file to a different location (or locations), while renaming it to fit our data structure and processing flow. As mentioned before, the files generated by Kinesis Firehose are structured in a pre-defined hierarchy, such as:

S3://your-bucket/your-prefix/2017/08/01/20/events-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz

All we need to do is parse the object name and restructure it as we see fit. In our case, we did the following (the event is an object received in the Lambda function with all the data about the object written to S3):

/*
	object key structure in the event object:
your-prefix/2017/08/01/20/event-4-2017-08-01-20-06-06-536f5c40-6893-4ee4-907d-81e4d3b09455.gz
	*/

let key_parts = event.Records[0].s3.object.key.split('/'); 

let event_type = key_parts[0];
let date = key_parts[1] + '-' + key_parts[2] + '-' + key_parts[3];
let hour = key_parts[4];
if (hour.indexOf('0') == 0) {
 		hour = parseInt(hour, 10) + '';
}
    
let parts1 = key_parts[5].split('-');
let minute = parts1[7];
if (minute.indexOf('0') == 0) {
        minute = parseInt(minute, 10) + '';
}

Now, we can redistribute the file to the two destinations we need—one for the minute processing task and the other for hourly aggregation:

    copyObjectToHourlyFolder(event, date, hour, minute)
        .then(copyObjectToMinuteFolder.bind(null, event, date, hour, minute))
        .then(addPartitionToSpectrum.bind(null, event, date, hour, minute))
        .then(deleteOldMinuteObjects.bind(null, event))
        .then(deleteStreamObject.bind(null, event))        
        .then(result => {
            callback(null, { message: 'done' });            
        })
        .catch(err => {
            console.error(err);
            callback(null, { message: err });            
        }); 

Kinesis Firehose stores the data in a temporary folder. We copy the object to another folder that holds the data for the last processed minute. This folder is connected to a small Redshift Spectrum table where the data is being processed without needing to scan a much larger dataset. We also copy the data to a folder that holds the data for the entire hour, to be later aggregated and converted to Parquet.

Because we partition the data by date and hour, we created a new partition on the Redshift Spectrum table if the processed minute is the first minute in the hour (that is, minute 0). We ran the following:

ALTER TABLE 
  spectrum.events 
ADD partition
  (date='2017-08-01', hour=0) 
  LOCATION 's3://nuviad-temp/events/2017-08-01/0/';

After the data is processed and added to the table, we delete the processed data from the temporary Kinesis Firehose storage and from the minute storage folder.

Migrating CSV to Parquet using AWS Glue and Amazon EMR

The simplest way we found to run an hourly job converting our CSV data to Parquet is using Lambda and AWS Glue (and thanks to the awesome AWS Big Data team for their help with this).

Creating AWS Glue jobs

What this simple AWS Glue script does:

  • Gets parameters for the job, date, and hour to be processed
  • Creates a Spark EMR context allowing us to run Spark code
  • Reads CSV data into a DataFrame
  • Writes the data as Parquet to the destination S3 bucket
  • Adds or modifies the Redshift Spectrum / Amazon Athena table partition for the table
import sys
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.job import Job
import boto3

## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME','day_partition_key', 'hour_partition_key', 'day_partition_value', 'hour_partition_value' ])

#day_partition_key = "partition_0"
#hour_partition_key = "partition_1"
#day_partition_value = "2017-08-01"
#hour_partition_value = "0"

day_partition_key = args['day_partition_key']
hour_partition_key = args['hour_partition_key']
day_partition_value = args['day_partition_value']
hour_partition_value = args['hour_partition_value']

print("Running for " + day_partition_value + "/" + hour_partition_value)

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

df = spark.read.option("delimiter","|").csv("s3://nuviad-temp/events/"+day_partition_value+"/"+hour_partition_value)
df.registerTempTable("data")

df1 = spark.sql("select _c0 as user_id, _c1 as campaign_id, _c2 as os, _c3 as ua, cast(_c4 as bigint) as ts, cast(_c5 as double) as billing from data")

df1.repartition(1).write.mode("overwrite").parquet("s3://nuviad-temp/parquet/"+day_partition_value+"/hour="+hour_partition_value)

client = boto3.client('athena', region_name='us-east-1')

response = client.start_query_execution(
    QueryString='alter table parquet_events add if not exists partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ')  location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

response = client.start_query_execution(
    QueryString='alter table parquet_events partition(' + day_partition_key + '=\'' + day_partition_value + '\',' + hour_partition_key + '=' + hour_partition_value + ') set location \'s3://nuviad-temp/parquet/' + day_partition_value + '/hour=' + hour_partition_value + '\'' ,
    QueryExecutionContext={
        'Database': 'spectrumdb'
    },
    ResultConfiguration={
        'OutputLocation': 's3://nuviad-temp/convertresults'
    }
)

job.commit()

Note: Because Redshift Spectrum and Athena both use the AWS Glue Data Catalog, we could use the Athena client to add the partition to the table.

Here are a few words about float, decimal, and double. Using decimal proved to be more challenging than we expected, as it seems that Redshift Spectrum and Spark use them differently. Whenever we used decimal in Redshift Spectrum and in Spark, we kept getting errors, such as:

S3 Query Exception (Fetch). Task failed due to an internal error. File 'https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parquet has an incompatible Parquet schema for column 's3://nuviad-events/events.lat'. Column type: DECIMAL(18, 8), Parquet schema:\noptional float lat [i:4 d:1 r:0]\n (https://s3-external-1.amazonaws.com/nuviad-temp/events/2017-08-01/hour=2/part-00017-48ae5b6b-906e-4875-8cde-bc36c0c6d0ca.c000.snappy.parq

We had to experiment with a few floating-point formats until we found that the only combination that worked was to define the column as double in the Spark code and float in Spectrum. This is the reason you see billing defined as float in Spectrum and double in the Spark code.

Creating a Lambda function to trigger conversion

Next, we created a simple Lambda function to trigger the AWS Glue script hourly using a simple Python code:

import boto3
import json
from datetime import datetime, timedelta
 
client = boto3.client('glue')
 
def lambda_handler(event, context):
    last_hour_date_time = datetime.now() - timedelta(hours = 1)
    day_partition_value = last_hour_date_time.strftime("%Y-%m-%d") 
    hour_partition_value = last_hour_date_time.strftime("%-H") 
    response = client.start_job_run(
    JobName='convertEventsParquetHourly',
    Arguments={
         '--day_partition_key': 'date',
         '--hour_partition_key': 'hour',
         '--day_partition_value': day_partition_value,
         '--hour_partition_value': hour_partition_value
         }
    )

Using Amazon CloudWatch Events, we trigger this function hourly. This function triggers an AWS Glue job named ‘convertEventsParquetHourly’ and runs it for the previous hour, passing job names and values of the partitions to process to AWS Glue.

Redshift Spectrum and Node.js

Our development stack is based on Node.js, which is well-suited for high-speed, light servers that need to process a huge number of transactions. However, a few limitations of the Node.js environment required us to create workarounds and use other tools to complete the process.

Node.js and Parquet

The lack of Parquet modules for Node.js required us to implement an AWS Glue/Amazon EMR process to effectively migrate data from CSV to Parquet. We would rather save directly to Parquet, but we couldn’t find an effective way to do it.

One interesting project in the works is the development of a Parquet NPM by Marc Vertes called node-parquet (https://www.npmjs.com/package/node-parquet). It is not in a production state yet, but we think it would be well worth following the progress of this package.

Timestamp data type

According to the Parquet documentation, Timestamp data are stored in Parquet as 64-bit integers. However, JavaScript does not support 64-bit integers, because the native number type is a 64-bit double, giving only 53 bits of integer range.

The result is that you cannot store Timestamp correctly in Parquet using Node.js. The solution is to store Timestamp as string and cast the type to Timestamp in the query. Using this method, we did not witness any performance degradation whatsoever.

Lessons learned

You can benefit from our trial-and-error experience.

Lesson #1: Data validation is critical

As mentioned earlier, a single corrupt entry in a partition can fail queries running against this partition, especially when using Parquet, which is harder to edit than a simple CSV file. Make sure that you validate your data before scanning it with Redshift Spectrum.

Lesson #2: Structure and partition data effectively

One of the biggest benefits of using Redshift Spectrum (or Athena for that matter) is that you don’t need to keep nodes up and running all the time. You pay only for the queries you perform and only for the data scanned per query.

Keeping different permutations of your data for different queries makes a lot of sense in this case. For example, you can partition your data by date and hour to run time-based queries, and also have another set partitioned by user_id and date to run user-based queries. This results in faster and more efficient performance of your data warehouse.

Storing data in the right format

Use Parquet whenever you can. The benefits of Parquet are substantial. Faster performance, less data to scan, and much more efficient columnar format. However, it is not supported out-of-the-box by Kinesis Firehose, so you need to implement your own ETL. AWS Glue is a great option.

Creating small tables for frequent tasks

When we started using Redshift Spectrum, we saw our Amazon Redshift costs jump by hundreds of dollars per day. Then we realized that we were unnecessarily scanning a full day’s worth of data every minute. Take advantage of the ability to define multiple tables on the same S3 bucket or folder, and create temporary and small tables for frequent queries.

Lesson #3: Combine Athena and Redshift Spectrum for optimal performance

Moving to Redshift Spectrum also allowed us to take advantage of Athena as both use the AWS Glue Data Catalog. Run fast and simple queries using Athena while taking advantage of the advanced Amazon Redshift query engine for complex queries using Redshift Spectrum.

Redshift Spectrum excels when running complex queries. It can push many compute-intensive tasks, such as predicate filtering and aggregation, down to the Redshift Spectrum layer, so that queries use much less of your cluster’s processing capacity.

Lesson #4: Sort your Parquet data within the partition

We achieved another performance improvement by sorting data within the partition using sortWithinPartitions(sort_field). For example:

df.repartition(1).sortWithinPartitions("campaign_id")…

Conclusion

We were extremely pleased with using Amazon Redshift as our core data warehouse for over three years. But as our client base and volume of data grew substantially, we extended Amazon Redshift to take advantage of scalability, performance, and cost with Redshift Spectrum.

Redshift Spectrum lets us scale to virtually unlimited storage, scale compute transparently, and deliver super-fast results for our users. With Redshift Spectrum, we store data where we want at the cost we want, and have the data available for analytics when our users need it with the performance they expect.


About the Author

With 7 years of experience in the AdTech industry and 15 years in leading technology companies, Rafi Ton is the founder and CEO of NUVIAD. He enjoys exploring new technologies and putting them to use in cutting edge products and services, in the real world generating real money. Being an experienced entrepreneur, Rafi believes in practical-programming and fast adaptation of new technologies to achieve a significant market advantage.

 

 

Presenting Amazon Sumerian: An easy way to create VR, AR, and 3D experiences

Post Syndicated from Tara Walker original https://aws.amazon.com/blogs/aws/launch-presenting-amazon-sumerian/

If you have had an opportunity to read any of my blog posts or attended any session I’ve conducted at various conferences, you are probably aware that I am definitively a geek girl. I am absolutely enamored with all of the latest advancements that have been made in technology areas like cloud, artificial intelligence, internet of things and the maker space, as well as, with virtual reality and augmented reality. In my opinion, it is a wonderful time to be a geek. All the things that we dreamed about building while we sweated through our algorithms and discrete mathematics classes or the technology we marveled at when watching Star Wars and Star Trek are now coming to fruition.  So hopefully this means it will only be a matter of time before I can hyperdrive to other galaxies in space, but until then I can at least build the 3D virtual reality and augmented reality characters and images like those featured in some of my favorite shows.

Amazon Sumerian provides tools and resources that allows anyone to create and run augmented reality (AR), virtual reality (VR), and 3D applications with ease.  With Sumerian, you can build multi-platform experiences that run on hardware like the Oculus, HTC Vive, and iOS devices using WebVR compatible browsers and with support for ARCore on Android devices coming soon.

This exciting new service, currently in preview, delivers features to allow you to design highly immersive and interactive 3D experiences from your browser. Some of these features are:

  • Editor: A web-based editor for constructing 3D scenes, importing assets, scripting interactions and special effects, with cross-platform publishing.
  • Object Library: a library of pre-built objects and templates.
  • Asset Import: Upload 3D assets to use in your scene. Sumerian supports importing FBX, OBJ, and coming soon Unity projects.
  • Scripting Library: provides a JavaScript scripting library via its 3D engine for advanced scripting capabilities.
  • Hosts: animated, lifelike 3D characters that can be customized for gender, voice, and language.
  • AWS Services Integration: baked in integration with Amazon Polly and Amazon Lex to add speech and natural language to into Sumerian hosts. Additionally, the scripting library can be used with AWS Lambda allowing use of the full range of AWS services.

Since Amazon Sumerian doesn’t require you to have 3D graphics or programming experience to build rich, interactive VR and AR scenes, let’s take a quick run to the Sumerian Dashboard and check it out.

From the Sumerian Dashboard, I can easily create a new scene with a push of a button.

A default view of the new scene opens and is displayed in the Sumerian Editor. With the Tara Blog Scene opened in the editor, I can easily import assets into my scene.

I’ll click the Import Asset button and pick an asset, View Room, to import into the scene. With the desired asset selected, I’ll click the Add button to import it.

Excellent, my asset was successfully imported into the Sumerian Editor and is shown in the Asset panel.  Now, I have the option to add the View Room object into my scene by selecting it in the Asset panel and then dragging it onto the editor’s canvas.

I’ll repeat the import asset process and this time I will add the Mannequin asset to the scene.

Additionally, with Sumerian, I can add scripting to Entity assets to make my scene even more exciting by adding a ScriptComponent to an entity and creating a script.  I can use the provided built-in scripts or create my own custom scripts. If I create a new custom script, I will get a blank script with some base JavaScript code that looks similar to the code below.

'use strict';
/* global sumerian */
//This is Me-- trying out the custom scripts - Tara

var setup = function (args, ctx) {
// Called when play mode starts.
};
var fixedUpdate = function (args, ctx) {
// Called on every physics update, after setup().
};
var update = function (args, ctx) {
// Called on every render frame, after setup().
};
var lateUpdate = function (args, ctx) {
// Called after all script "update" methods in the scene has been called.
};
var cleanup = function (args, ctx) {
// Called when play mode stops.
};
var parameters = [];

Very cool, I just created a 3D scene using Amazon Sumerian in a matter of minutes and I have only scratched the surface.

Summary

The Amazon Sumerian service enables you to create, build, and run virtual reality (VR), augmented reality (AR), and 3D applications with ease.  You don’t need any 3D graphics or specialized programming knowledge to get started building scenes and immersive experiences.  You can import FBX, OBJ, and Unity projects in Sumerian, as well as upload your own 3D assets for use in your scene. In addition, you can create digital characters to narrate your scene and with these digital assets, you have choices for the character’s appearance, speech and behavior.

You can learn more about Amazon Sumerian and sign up for the preview to get started with the new service on the product page.  I can’t wait to see what rich experiences you all will build.

Tara

 

UI Testing at Scale with AWS Lambda

Post Syndicated from Stas Neyman original https://aws.amazon.com/blogs/devops/ui-testing-at-scale-with-aws-lambda/

This is a guest blog post by Wes Couch and Kurt Waechter from the Blackboard Internal Product Development team about their experience using AWS Lambda.

One year ago, one of our UI test suites took hours to run. Last month, it took 16 minutes. Today, it takes 39 seconds. Here’s how we did it.

The backstory:

Blackboard is a global leader in delivering robust and innovative education software and services to clients in higher education, government, K12, and corporate training. We have a large product development team working across the globe in at least 10 different time zones, with an internal tools team providing support for quality and workflows. We have been using Selenium Webdriver to perform automated cross-browser UI testing since 2007. Because we are now practicing continuous delivery, the automated UI testing challenge has grown due to the faster release schedule. On top of that, every commit made to each branch triggers an execution of our automated UI test suite. If you have ever implemented an automated UI testing infrastructure, you know that it can be very challenging to scale and maintain. Although there are services that are useful for testing different browser/OS combinations, they don’t meet our scale needs.

It used to take three hours to synchronously run our functional UI suite, which revealed the obvious need for parallel execution. Previously, we used Mesos to orchestrate a Selenium Grid Docker container for each test run. This way, we were able to run eight concurrent threads for test execution, which took an average of 16 minutes. Although this setup is fine for a single workflow, the cracks started to show when we reached the scale required for Blackboard’s mature product lines. Going beyond eight concurrent sessions on a single container introduced performance problems that impact the reliability of tests (for example, issues in Webdriver or the browser popping up frequently). We tried Mesos and considered Kubernetes for Selenium Grid orchestration, but the answer to scaling a Selenium Grid was to think smaller, not larger. This led to our breakthrough with AWS Lambda.

The solution:

We started using AWS Lambda for UI testing because it doesn’t require costly infrastructure or countless man hours to maintain. The steps we outline in this blog post took one work day, from inception to implementation. By simply packaging the UI test suite into a Lambda function, we can execute these tests in parallel on a massive scale. We use a custom JUnit test runner that invokes the Lambda function with a request to run each test from the suite. The runner then aggregates the results returned from each Lambda test execution.

Selenium is the industry standard for testing UI at scale. Although there are other options to achieve the same thing in Lambda, we chose this mature suite of tools. Selenium is backed by Google, Firefox, and others to help the industry drive their browsers with code. This makes Lambda and Selenium a compelling stack for achieving UI testing at scale.

Making Chrome Run in Lambda

Currently, Chrome for Linux will not run in Lambda due to an absent mount point. By rebuilding Chrome with a slight modification, as Marco Lüthy originally demonstrated, you can run it inside Lambda anyway! It took about two hours to build the current master branch of Chromium to build on a c4.4xlarge. Unfortunately, the current version of ChromeDriver, 2.33, does not support any version of Chrome above 62, so we’ll be using Marco’s modified version of version 60 for the near future.

Required System Libraries

The Lambda runtime environment comes with a subset of common shared libraries. This means we need to include some extra libraries to get Chrome and ChromeDriver to work. Anything that exists in the java resources folder during compile time is included in the base directory of the compiled jar file. When this jar file is deployed to Lambda, it is placed in the /var/task/ directory. This allows us to simply place the libraries in the java resources folder under a folder named lib/ so they are right where they need to be when the Lambda function is invoked.

To get these libraries, create an EC2 instance and choose the Amazon Linux AMI.

Next, use ssh to connect to the server. After you connect to the new instance, search for the libraries to find their locations.

sudo find / -name libgconf-2.so.4
sudo find / -name libORBit-2.so.0

Now that you have the locations of the libraries, copy these files from the EC2 instance and place them in the java resources folder under lib/.

Packaging the Tests

To deploy the test suite to Lambda, we used a simple Gradle tool called ShadowJar, which is similar to the Maven Shade Plugin. It packages the libraries and dependencies inside the jar that is built. Usually test dependencies and sources aren’t included in a jar, but for this instance we want to include them. To include the test dependencies, add this section to the build.gradle file.

shadowJar {
   from sourceSets.test.output
   configurations = [project.configurations.testRuntime]
}

Deploying the Test Suite

Now that our tests are packaged with the dependencies in a jar, we need to get them into a running Lambda function. We use  simple SAM  templates to upload the packaged jar into S3, and then deploy it to Lambda with our settings.

{
   "AWSTemplateFormatVersion": "2010-09-09",
   "Transform": "AWS::Serverless-2016-10-31",
   "Resources": {
       "LambdaTestHandler": {
           "Type": "AWS::Serverless::Function",
           "Properties": {
               "CodeUri": "./build/libs/your-test-jar-all.jar",
               "Runtime": "java8",
               "Handler": "com.example.LambdaTestHandler::handleRequest",
               "Role": "<YourLambdaRoleArn>",
               "Timeout": 300,
               "MemorySize": 1536
           }
       }
   }
}

We use the maximum timeout available to ensure our tests have plenty of time to run. We also use the maximum memory size because this ensures our Lambda function can support Chrome and other resources required to run a UI test.

Specifying the handler is important because this class executes the desired test. The test handler should be able to receive a test class and method. With this information it will then execute the test and respond with the results.

public LambdaTestResult handleRequest(TestRequest testRequest, Context context) {
   LoggerContainer.LOGGER = new Logger(context.getLogger());
  
   BlockJUnit4ClassRunner runner = getRunnerForSingleTest(testRequest);
  
   Result result = new JUnitCore().run(runner);

   return new LambdaTestResult(result);
}

Creating a Lambda-Compatible ChromeDriver

We provide developers with an easily accessible ChromeDriver for local test writing and debugging. When we are running tests on AWS, we have configured ChromeDriver to run them in Lambda.

To configure ChromeDriver, we first need to tell ChromeDriver where to find the Chrome binary. Because we know that ChromeDriver is going to be unzipped into the root task directory, we should point the ChromeDriver configuration at that location.

The settings for getting ChromeDriver running are mostly related to Chrome, which must have its working directories pointed at the tmp/ folder.

Start with the default DesiredCapabilities for ChromeDriver, and then add the following settings to enable your ChromeDriver to start in Lambda.

public ChromeDriver createLambdaChromeDriver() {
   ChromeOptions options = new ChromeOptions();

   // Set the location of the chrome binary from the resources folder
   options.setBinary("/var/task/chrome");

   // Include these settings to allow Chrome to run in Lambda
   options.addArguments("--disable-gpu");
   options.addArguments("--headless");
   options.addArguments("--window-size=1366,768");
   options.addArguments("--single-process");
   options.addArguments("--no-sandbox");
   options.addArguments("--user-data-dir=/tmp/user-data");
   options.addArguments("--data-path=/tmp/data-path");
   options.addArguments("--homedir=/tmp");
   options.addArguments("--disk-cache-dir=/tmp/cache-dir");
  
   DesiredCapabilities desiredCapabilities = DesiredCapabilities.chrome();
   desiredCapabilities.setCapability(ChromeOptions.CAPABILITY, options);
  
   return new ChromeDriver(desiredCapabilities);
}

Executing Tests in Parallel

You can approach parallel test execution in Lambda in many different ways. Your approach depends on the structure and design of your test suite. For our solution, we implemented a custom test runner that uses reflection and JUnit libraries to create a list of test cases we want run. When we have the list, we create a TestRequest object to pass into the Lambda function that we have deployed. In this TestRequest, we place the class name, test method, and the test run identifier. When the Lambda function receives this TestRequest, our LambdaTestHandler generates and runs the JUnit test. After the test is complete, the test result is sent to the test runner. The test runner compiles a result after all of the tests are complete. By executing the same Lambda function multiple times with different test requests, we can effectively run the entire test suite in parallel.

To get screenshots and other test data, we pipe those files during test execution to an S3 bucket under the test run identifier prefix. When the tests are complete, we link the files to each test execution in the report generated from the test run. This lets us easily investigate test executions.

Pro Tip: Dynamically Loading Binaries

AWS Lambda has a limit of 250 MB of uncompressed space for packaged Lambda functions. Because we have libraries and other dependencies to our test suite, we hit this limit when we tried to upload a function that contained Chrome and ChromeDriver (~140 MB). This test suite was not originally intended to be used with Lambda. Otherwise, we would have scrutinized some of the included libraries. To get around this limit, we used the Lambda functions temporary directory, which allows up to 500 MB of space at runtime. Downloading these binaries at runtime moves some of that space requirement into the temporary directory. This allows more room for libraries and dependencies. You can do this by grabbing Chrome and ChromeDriver from an S3 bucket and marking them as executable using built-in Java libraries. If you take this route, be sure to point to the new location for these executables in order to create a ChromeDriver.

private static void downloadS3ObjectToExecutableFile(String key) throws IOException {
   File file = new File("/tmp/" + key);

   GetObjectRequest request = new GetObjectRequest("s3-bucket-name", key);

   FileUtils.copyInputStreamToFile(s3client.getObject(request).getObjectContent(), file);
   file.setExecutable(true);
}

Lambda-Selenium Project Source

We have compiled an open source example that you can grab from the Blackboard Github repository. Grab the code and try it out!

https://blackboard.github.io/lambda-selenium/

Conclusion

One year ago, one of our UI test suites took hours to run. Last month, it took 16 minutes. Today, it takes 39 seconds. Thanks to AWS Lambda, we can reduce our build times and perform automated UI testing at scale!

How to Enable Caching for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/how-to-enable-caching-for-aws-codebuild/

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

A typical application build process includes phases like preparing the environment, updating the configuration, downloading dependencies, running unit tests, and finally, packaging the built artifact.

Downloading dependencies is a critical phase in the build process. These dependent files can range in size from a few KBs to multiple MBs. Because most of the dependent files do not change frequently between builds, you can noticeably reduce your build time by caching dependencies.

In this post, I will show you how to enable caching for AWS CodeBuild.

Requirements

  • Create an Amazon S3 bucket for storing cache archives (You can use existing s3 bucket as well).
  • Create a GitHub account (if you don’t have one).

Create a sample build project:

1. Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/.

2. If a welcome page is displayed, choose Get started.

If a welcome page is not displayed, on the navigation pane, choose Build projects, and then choose Create project.

3. On the Configure your project page, for Project name, type a name for this build project. Build project names must be unique across each AWS account.

4. In Source: What to build, for Source provider, choose GitHub.

5. In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild.

  • For Operating system, choose Ubuntu.
  • For Runtime, choose Java.
  • For Version,  choose aws/codebuild/java:openjdk-8.
  • For Build specification, select Insert build commands.

Note: The build specification file (buildspec.yml) can be configured in two ways. You can package it along with your source root directory, or you can override it by using a project environment configuration. In this example, I will use the override option and will use the console editor to specify the build specification.

6. Under Build commands, click Switch to editor to enter the build specification.

Copy the following text.

version: 0.2

phases:
  build:
    commands:
      - mvn install
      
cache:
  paths:
    - '/root/.m2/**/*'

Note: The cache section in the build specification instructs AWS CodeBuild about the paths to be cached. Like the artifacts section, the cache paths are relative to $CODEBUILD_SRC_DIR and specify the directories to be cached. In this example, Maven stores the downloaded dependencies to the /root/.m2/ folder, but other tools use different folders. For example, pip uses the /root/.cache/pip folder, and Gradle uses the /root/.gradle/caches folder. You might need to configure the cache paths based on your language platform.

7. In Artifacts: Where to put the artifacts from this build project:

  • For Type, choose No artifacts.

8. In Cache:

  • For Type, choose Amazon S3.
  • For Bucket, choose your S3 bucket.
  • For Path prefix, type cache/archives/

9. In Service role, the Create a service role in your account option will display a default role name.  You can accept the default name or type your own.

If you already have an AWS CodeBuild service role, choose Choose an existing service role from your account.

10. Choose Continue.

11. On the Review page, to run a build, choose Save and build.

Review build and cache behavior:

Let us review our first build for the project.

In the first run, where no cache exists, overall build time would look something like below (notice the time for DOWNLOAD_SOURCE, BUILD and POST_BUILD):

If you check the build logs, you will see log entries for dependency downloads. The dependencies are downloaded directly from configured external repositories. At the end of the log, you will see an entry for the cache uploaded to your S3 bucket.

Let’s review the S3 bucket for the cached archive. You’ll see the cache from our first successful build is uploaded to the configured S3 path.

Let’s try another build with the same CodeBuild project. This time the build should pick up the dependencies from the cache.

In the second run, there was a cache hit (cache was generated from the first run):

You’ll notice a few things:

  1. DOWNLOAD_SOURCE took slightly longer. Because, in addition to the source code, this time the build also downloaded the cache from user’s s3 bucket.
  2. BUILD time was faster. As the dependencies didn’t need to get downloaded, but were reused from cache.
  3. POST_BUILD took slightly longer, but was relatively the same.

Overall, build duration was improved with cache.

Best practices for cache

  • By default, the cache archive is encrypted on the server side with the customer’s artifact KMS key.
  • You can expire the cache by manually removing the cache archive from S3. Alternatively, you can expire the cache by using an S3 lifecycle policy.
  • You can override cache behavior by updating the project. You can use the AWS CodeBuild the AWS CodeBuild console, AWS CLI, or AWS SDKs to update the project. You can also invalidate cache setting by using the new InvalidateProjectCache API. This API forces a new InvalidationKey to be generated, ensuring that future builds receive an empty cache. This API does not remove the existing cache, because this could cause inconsistencies with builds currently in flight.
  • The cache can be enabled for any folders in the build environment, but we recommend you only cache dependencies/files that will not change frequently between builds. Also, to avoid unexpected application behavior, don’t cache configuration and sensitive information.

Conclusion

In this blog post, I showed you how to enable and configure cache setting for AWS CodeBuild. As you see, this can save considerable build time. It also improves resiliency by avoiding external network connections to an artifact repository.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Access Resources in a VPC from AWS CodeBuild Builds

Post Syndicated from John Pignata original https://aws.amazon.com/blogs/devops/access-resources-in-a-vpc-from-aws-codebuild-builds/

John Pignata, Startup Solutions Architect, Amazon Web Services

In this blog post we’re going to discuss a new AWS CodeBuild feature that is available starting today. CodeBuild builds can now access resources in a VPC directly without these resources being exposed to the public internet. These resources include Amazon Relational Database Service (Amazon RDS) databases, Amazon ElastiCache clusters, internal services running on Amazon Elastic Compute Cloud (Amazon EC2), and Amazon EC2 Container Service (Amazon ECS), or any service endpoints that are only reachable from within a specific VPC.

CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. As part of the build process, developers often require access to resources that should be isolated from the public Internet. Now CodeBuild builds can be optionally configured to have VPC connectivity and access these resources directly.

Accessing Resources in a VPC

You can configure builds to have access to a VPC when you create a CodeBuild project or you can update an existing CodeBuild project with VPC configuration attributes. Here’s how it looks in the console:

 

To configure VPC connectivity: select a VPC, one or more subnets within that VPC, and one or more VPC security groups that CodeBuild should apply when attaching to your VPC. Once configured, commands running as part of your build will be able to access resources in your VPC without transiting across the public Internet.

Use Cases

The availability of VPC connectivity from CodeBuild builds unlocks many potential uses. For example, you can:

  • Run integration tests from your build against data in an Amazon RDS instance that’s isolated on a private subnet.
  • Query data in an ElastiCache cluster directly from tests.
  • Interact with internal web services hosted on Amazon EC2, Amazon ECS, or services that use internal Elastic Load Balancing.
  • Retrieve dependencies from self-hosted, internal artifact repositories such as PyPI for Python, Maven for Java, npm for Node.js, and so on.
  • Access objects in an Amazon S3 bucket configured to allow access only through a VPC endpoint.
  • Query external web services that require fixed IP addresses through the Elastic IP address of the NAT gateway associated with your subnet(s).

… and more! Your builds can now access any resource that’s hosted in your VPC without any compromise on network isolation.

Internet Connectivity

CodeBuild requires access to resources on the public Internet to successfully execute builds. At a minimum, it must be able to reach your source repository system (such as AWS CodeCommit, GitHub, Bitbucket), Amazon Simple Storage Service (Amazon S3) to deliver build artifacts, and Amazon CloudWatch Logs to stream logs from the build process. The interface attached to your VPC will not be assigned a public IP address so to enable Internet access from your builds, you will need to set up a managed NAT Gateway or NAT instance for the subnets you configure. You must also ensure your security groups allow outbound access to these services.

IP Address Space

Each running build will be assigned an IP address from one of the subnets in your VPC that you designate for CodeBuild to use. As CodeBuild scales to meet your build volume, ensure that you select subnets with enough address space to accommodate your expected number of concurrent builds.

Service Role Permissions

CodeBuild requires new permissions in order to manage network interfaces on your VPCs. If you create a service role for your new projects, these permissions will be included in that role’s policy automatically. For existing service roles, you can edit the policy document to include the additional actions. For the full policy document to apply to your service role, see Advanced Setup in the CodeBuild documentation.

For more information, see VPC Support in the CodeBuild documentation. We hope you find the ability to access internal resources on a VPC useful in your build processes! If you have any questions or feedback, feel free to reach out to us through the AWS CodeBuild forum or leave a comment!

Using AWS CodeCommit Pull Requests to request code reviews and discuss code

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/using-aws-codecommit-pull-requests-to-request-code-reviews-and-discuss-code/

Thank you to Michael Edge, Senior Cloud Architect, for a great blog on CodeCommit pull requests.

~~~~~~~

AWS CodeCommit is a fully managed service for securely hosting private Git repositories. CodeCommit now supports pull requests, which allows repository users to review, comment upon, and interactively iterate on code changes. Used as a collaboration tool between team members, pull requests help you to review potential changes to a CodeCommit repository before merging those changes into the repository. Each pull request goes through a simple lifecycle, as follows:

  • The new features to be merged are added as one or more commits to a feature branch. The commits are not merged into the destination branch.
  • The pull request is created, usually from the difference between two branches.
  • Team members review and comment on the pull request. The pull request might be updated with additional commits that contain changes made in response to comments, or include changes made to the destination branch.
  • Once team members are happy with the pull request, it is merged into the destination branch. The commits are applied to the destination branch in the same order they were added to the pull request.

Commenting is an integral part of the pull request process, and is used to collaborate between the developers and the reviewer. Reviewers add comments and questions to a pull request during the review process, and developers respond to these with explanations. Pull request comments can be added to the overall pull request, a file within the pull request, or a line within a file.

To make the comments more useful, sign in to the AWS Management Console as an AWS Identity and Access Management (IAM) user. The username will then be associated with the comment, indicating the owner of the comment. Pull request comments are a great quality improvement tool as they allow the entire development team visibility into what reviewers are looking for in the code. They also serve as a record of the discussion between team members at a point in time, and shouldn’t be deleted.

AWS CodeCommit is also introducing the ability to add comments to a commit, another useful collaboration feature that allows team members to discuss code changed as part of a commit. This helps you discuss changes made in a repository, including why the changes were made, whether further changes are necessary, or whether changes should be merged. As is the case with pull request comments, you can comment on an overall commit, on a file within a commit, or on a specific line or change within a file, and other repository users can respond to your comments. Comments are not restricted to commits, they can also be used to comment on the differences between two branches, or between two tags. Commit comments are separate from pull request comments, i.e. you will not see commit comments when reviewing a pull request – you will only see pull request comments.

A pull request example

Let’s get started by running through an example. We’ll take a typical pull request scenario and look at how we’d use CodeCommit and the AWS Management Console for each of the steps.

To try out this scenario, you’ll need:

  • An AWS CodeCommit repository with some sample code in the master branch. We’ve provided sample code below.
  • Two AWS Identity and Access Management (IAM) users, both with the AWSCodeCommitPowerUser managed policy applied to them.
  • Git installed on your local computer, and access configured for AWS CodeCommit.
  • A clone of the AWS CodeCommit repository on your local computer.

In the course of this example, you’ll sign in to the AWS CodeCommit console as one IAM user to create the pull request, and as the other IAM user to review the pull request. To learn more about how to set up your IAM users and how to connect to AWS CodeCommit with Git, see the following topics:

  • Information on creating an IAM user with AWS Management Console access.
  • Instructions on how to access CodeCommit using Git.
  • If you’d like to use the same ‘hello world’ application as used in this article, here is the source code:
package com.amazon.helloworld;

public class Main {
	public static void main(String[] args) {

		System.out.println("Hello, world");
	}
}

The scenario below uses the us-east-2 region.

Creating the branches

Before we jump in and create a pull request, we’ll need at least two branches. In this example, we’ll follow a branching strategy similar to the one described in GitFlow. We’ll create a new branch for our feature from the main development branch (the default branch). We’ll develop the feature in the feature branch. Once we’ve written and tested the code for the new feature in that branch, we’ll create a pull request that contains the differences between the feature branch and the main development branch. Our team lead (the second IAM user) will review the changes in the pull request. Once the changes have been reviewed, the feature branch will be merged into the development branch.

Figure 1: Pull request link

Sign in to the AWS CodeCommit console with the IAM user you want to use as the developer. You can use an existing repository or you can go ahead and create a new one. We won’t be merging any changes to the master branch of your repository, so it’s safe to use an existing repository for this example. You’ll find the Pull requests link has been added just above the Commits link (see Figure 1), and below Commits you’ll find the Branches link. Click Branches and create a new branch called ‘develop’, branched from the ‘master’ branch. Then create a new branch called ‘feature1’, branched from the ‘develop’ branch. You’ll end up with three branches, as you can see in Figure 2. (Your repository might contain other branches in addition to the three shown in the figure).

Figure 2: Create a feature branch

If you haven’t cloned your repo yet, go to the Code link in the CodeCommit console and click the Connect button. Follow the instructions to clone your repo (detailed instructions are here). Open a terminal or command line and paste the git clone command supplied in the Connect instructions for your repository. The example below shows cloning a repository named codecommit-demo:

git clone https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo

If you’ve previously cloned the repo you’ll need to update your local repo with the branches you created. Open a terminal or command line and make sure you’re in the root directory of your repo, then run the following command:

git remote update origin

You’ll see your new branches pulled down to your local repository.

$ git remote update origin
Fetching origin
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
 * [new branch]      develop    -> origin/develop
 * [new branch]      feature1   -> origin/feature1

You can also see your new branches by typing:

git branch --all

$ git branch --all
* master
  remotes/origin/develop
  remotes/origin/feature1
  remotes/origin/master

Now we’ll make a change to the ‘feature1’ branch. Open a terminal or command line and check out the feature1 branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Make code changes

Edit a file in the repo using your favorite editor and save the changes. Commit your changes to the local repository, and push your changes to CodeCommit. For example:

git commit -am 'added new feature'
git push origin feature1

$ git commit -am 'added new feature'
[feature1 8f6cb28] added new feature
1 file changed, 1 insertion(+), 1 deletion(-)

$ git push origin feature1
Counting objects: 9, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (4/4), done.
Writing objects: 100% (9/9), 617 bytes | 617.00 KiB/s, done.
Total 9 (delta 2), reused 0 (delta 0)
To https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   2774a53..8f6cb28  feature1 -> feature1

Creating the pull request

Now we have a ‘feature1’ branch that differs from the ‘develop’ branch. At this point we want to merge our changes into the ‘develop’ branch. We’ll create a pull request to notify our team members to review our changes and check whether they are ready for a merge.

In the AWS CodeCommit console, click Pull requests. Click Create pull request. On the next page select ‘develop’ as the destination branch and ‘feature1’ as the source branch. Click Compare. CodeCommit will check for merge conflicts and highlight whether the branches can be automatically merged using the fast-forward option, or whether a manual merge is necessary. A pull request can be created in both situations.

Figure 3: Create a pull request

After comparing the two branches, the CodeCommit console displays the information you’ll need in order to create the pull request. In the ‘Details’ section, the ‘Title’ for the pull request is mandatory, and you may optionally provide comments to your reviewers to explain the code change you have made and what you’d like them to review. In the ‘Notifications’ section, there is an option to set up notifications to notify subscribers of changes to your pull request. Notifications will be sent on creation of the pull request as well as for any pull request updates or comments. And finally, you can review the changes that make up this pull request. This includes both the individual commits (a pull request can contain one or more commits, available in the Commits tab) as well as the changes made to each file, i.e. the diff between the two branches referenced by the pull request, available in the Changes tab. After you have reviewed this information and added a title for your pull request, click the Create button. You will see a confirmation screen, as shown in Figure 4, indicating that your pull request has been successfully created, and can be merged without conflicts into the ‘develop’ branch.

Figure 4: Pull request confirmation page

Reviewing the pull request

Now let’s view the pull request from the perspective of the team lead. If you set up notifications for this CodeCommit repository, creating the pull request would have sent an email notification to the team lead, and he/she can use the links in the email to navigate directly to the pull request. In this example, sign in to the AWS CodeCommit console as the IAM user you’re using as the team lead, and click Pull requests. You will see the same information you did during creation of the pull request, plus a record of activity related to the pull request, as you can see in Figure 5.

Figure 5: Team lead reviewing the pull request

Commenting on the pull request

You now perform a thorough review of the changes and make a number of comments using the new pull request comment feature. To gain an overall perspective on the pull request, you might first go to the Commits tab and review how many commits are included in this pull request. Next, you might visit the Changes tab to review the changes, which displays the differences between the feature branch code and the develop branch code. At this point, you can add comments to the pull request as you work through each of the changes. Let’s go ahead and review the pull request. During the review, you can add review comments at three levels:

  • The overall pull request
  • A file within the pull request
  • An individual line within a file

The overall pull request
In the Changes tab near the bottom of the page you’ll see a ‘Comments on changes’ box. We’ll add comments here related to the overall pull request. Add your comments as shown in Figure 6 and click the Save button.

Figure 6: Pull request comment

A specific file in the pull request
Hovering your mouse over a filename in the Changes tab will cause a blue ‘comments’ icon to appear to the left of the filename. Clicking the icon will allow you to enter comments specific to this file, as in the example in Figure 7. Go ahead and add comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 7: File comment

A specific line in a file in the pull request
A blue ‘comments’ icon will appear as you hover over individual lines within each file in the pull request, allowing you to create comments against lines that have been added, removed or are unchanged. In Figure 8, you add comments against a line that has been added to the source code, encouraging the developer to review the naming standards. Go ahead and add line comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 8: Line comment

A pull request that has been commented at all three levels will look similar to Figure 9. The pull request comment is shown expanded in the ‘Comments on changes’ section, while the comments at file and line level are shown collapsed. A ‘comment’ icon indicates that comments exist at file and line level. Clicking the icon will expand and show the comment. Since you are expecting the developer to make further changes based on your comments, you won’t merge the pull request at this stage, but will leave it open awaiting feedback. Each comment you made results in a notification being sent to the developer, who can respond to the comments. This is great for remote working, where developers and team lead may be in different time zones.

Figure 9: Fully commented pull request

Adding a little complexity

A typical development team is going to be creating pull requests on a regular basis. It’s highly likely that the team lead will merge other pull requests into the ‘develop’ branch while pull requests on feature branches are in the review stage. This may result in a change to the ‘Mergable’ status of a pull request. Let’s add this scenario into the mix and check out how a developer will handle this.

To test this scenario, we could create a new pull request and ask the team lead to merge this to the ‘develop’ branch. But for the sake of simplicity we’ll take a shortcut. Clone your CodeCommit repo to a new folder, switch to the ‘develop’ branch, and make a change to one of the same files that were changed in your pull request. Make sure you change a line of code that was also changed in the pull request. Commit and push this back to CodeCommit. Since you’ve just changed a line of code in the ‘develop’ branch that has also been changed in the ‘feature1’ branch, the ‘feature1’ branch cannot be cleanly merged into the ‘develop’ branch. Your developer will need to resolve this merge conflict.

A developer reviewing the pull request would see the pull request now looks similar to Figure 10, with a ‘Resolve conflicts’ status rather than the ‘Mergable’ status it had previously (see Figure 5).

Figure 10: Pull request with merge conflicts

Reviewing the review comments

Once the team lead has completed his review, the developer will review the comments and make the suggested changes. As a developer, you’ll see the list of review comments made by the team lead in the pull request Activity tab, as shown in Figure 11. The Activity tab shows the history of the pull request, including commits and comments. You can reply to the review comments directly from the Activity tab, by clicking the Reply button, or you can do this from the Changes tab. The Changes tab shows the comments for the latest commit, as comments on previous commits may be associated with lines that have changed or been removed in the current commit. Comments for previous commits are available to view and reply to in the Activity tab.

In the Activity tab, use the shortcut link (which looks like this </>) to move quickly to the source code associated with the comment. In this example, you will make further changes to the source code to address the pull request review comments, so let’s go ahead and do this now. But first, you will need to resolve the ‘Resolve conflicts’ status.

Figure 11: Pull request activity

Resolving the ‘Resolve conflicts’ status

The ‘Resolve conflicts’ status indicates there is a merge conflict between the ‘develop’ branch and the ‘feature1’ branch. This will require manual intervention to restore the pull request back to the ‘Mergable’ state. We will resolve this conflict next.

Open a terminal or command line and check out the develop branch by running the following command:

git checkout develop

$ git checkout develop
Switched to branch 'develop'
Your branch is up-to-date with 'origin/develop'.

To incorporate the changes the team lead made to the ‘develop’ branch, merge the remote ‘develop’ branch with your local copy:

git pull

$ git pull
remote: Counting objects: 9, done.
Unpacking objects: 100% (9/9), done.
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   af13c82..7b36f52  develop    -> origin/develop
Updating af13c82..7b36f52
Fast-forward
 src/main/java/com/amazon/helloworld/Main.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

Then checkout the ‘feature1’ branch:

git checkout feature1

$ git checkout feature1
Switched to branch 'feature1'
Your branch is up-to-date with 'origin/feature1'.

Now merge the changes from the ‘develop’ branch into your ‘feature1’ branch:

git merge develop

$ git merge develop
Auto-merging src/main/java/com/amazon/helloworld/Main.java
CONFLICT (content): Merge conflict in src/main/java/com/amazon/helloworld/Main.java
Automatic merge failed; fix conflicts and then commit the result.

Yes, this fails. The file Main.java has been changed in both branches, resulting in a merge conflict that can’t be resolved automatically. However, Main.java will now contain markers that indicate where the conflicting code is, and you can use these to resolve the issues manually. Edit Main.java using your favorite IDE, and you’ll see it looks something like this:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

<<<<<<< HEAD
        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earthling. Today's date is: " + todaysdate);
=======
      System.out.println("Hello, earth");
>>>>>>> develop
   }
}

The code between HEAD and ‘===’ is the code the developer added in the ‘feature1’ branch (HEAD represents ‘feature1’ because this is the current checked out branch). The code between ‘===’ and ‘>>> develop’ is the code added to the ‘develop’ branch by the team lead. We’ll resolve the conflict by manually merging both changes, resulting in an updated Main.java:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysdate);
   }
}

After saving the change you can add and commit it to your local repo:

git add src/
git commit -m 'fixed merge conflict by merging changes'

Fixing issues raised by the reviewer

Now you are ready to address the comments made by the team lead. If you are no longer pointing to the ‘feature1’ branch, check out the ‘feature1’ branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Edit the source code in your favorite IDE and make the changes to address the comments. In this example, the developer has updated the source code as follows:

package com.amazon.helloworld;

import java.util.*;

/**
 *  This class prints a hello world message
 *
 * @author Michael Edge
 * @see HelloEarth
 * @version 1.0
 */

public class Main {
   public static void main(String[] args) {

        Date todaysDate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysDate);
   }
}

After saving the changes, commit and push to the CodeCommit ‘feature1’ branch as you did previously:

git commit -am 'updated based on review comments'
git push origin feature1

Responding to the reviewer

Now that you’ve fixed the code issues you will want to respond to the review comments. In the AWS CodeCommit console, check that your latest commit appears in the pull request Commits tab. You now have a pull request consisting of more than one commit. The pull request in Figure 12 has four commits, which originated from the following activities:

  • 8th Nov: the original commit used to initiate this pull request
  • 10th Nov, 3 hours ago: the commit by the team lead to the ‘develop’ branch, merged into our ‘feature1’ branch
  • 10th Nov, 24 minutes ago: the commit by the developer that resolved the merge conflict
  • 10th Nov, 4 minutes ago: the final commit by the developer addressing the review comments

Figure 12: Pull request with multiple commits

Let’s reply to the review comments provided by the team lead. In the Activity tab, reply to the pull request comment and save it, as shown in Figure 13.

Figure 13: Replying to a pull request comment

At this stage, your code has been committed and you’ve updated your pull request comments, so you are ready for a final review by the team lead.

Final review

The team lead reviews the code changes and comments made by the developer. As team lead, you own the ‘develop’ branch and it’s your decision on whether to merge the changes in the pull request into the ‘develop’ branch. You can close the pull request with or without merging using the Merge and Close buttons at the bottom of the pull request page (see Figure 13). Clicking Close will allow you to add comments on why you are closing the pull request without merging. Merging will perform a fast-forward merge, incorporating the commits referenced by the pull request. Let’s go ahead and click the Merge button to merge the pull request into the ‘develop’ branch.

Figure 14: Merging the pull request

After merging a pull request, development of that feature is complete and the feature branch is no longer needed. It’s common practice to delete the feature branch after merging. CodeCommit provides a check box during merge to automatically delete the associated feature branch, as seen in Figure 14. Clicking the Merge button will merge the pull request into the ‘develop’ branch, as shown in Figure 15. This will update the status of the pull request to ‘Merged’, and will close the pull request.

Conclusion

This blog has demonstrated how pull requests can be used to request a code review, and enable reviewers to get a comprehensive summary of what is changing, provide feedback to the author, and merge the code into production. For more information on pull requests, see the documentation.